Publication
2024
Pengfei Lin, Ehsan Javanmardi, Yuze Jiang, Manabu Tsukada, "A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety", In: 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV), Dubai, UAE, 2024.Proceedings Article | BibTeX
@inproceedings{Lin2024c,
title = {A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Yuze Jiang and Manabu Tsukada},
year = {2024},
date = {2024-12-12},
urldate = {2024-12-12},
booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)},
address = {Dubai, UAE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pengfei Lin, Ehsan Javanmardi, Manabu Tsukada, "Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles", In: IEEE Transactions on Vehicular Technology, vol. 73, iss. 7, pp. 9747-9762, 2024, ISSN: 0018-9545.Journal Article | Abstract | BibTeX | Links:
@article{Lin2024b,
title = {Clothoid Curve-based Emergency Stopping Path-Planning with Adaptive Potential Field for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1109/TVT.2024.3380745},
issn = {0018-9545},
year = {2024},
date = {2024-07-24},
urldate = {2024-03-22},
journal = {IEEE Transactions on Vehicular Technology},
volume = {73},
issue = {7},
pages = {9747-9762},
abstract = {Potential Field-based path planning methods are widely embraced in the context of autonomous vehicles due to their real-time efficiency and simplicity. While the potential field effectively enforces a rigid road boundary to keep the vehicle within the confines of the road, it can lead to the “blind alley” problem caused by local minima in specific high- speed scenarios, resulting in indecision, erratic behavior, or even accidents. Therefore, the objective of this research is to anticipate and address the aforementioned problem in order to proactively avoid potential collisions. We have also found that existing methods do not offer a root cause analysis or practical solutions for this issue, which limits the practicality of the potential field in handling complicated traffic situations. In this paper, we propose an Emergency-Stopping Path Planning (ESPP) approach that incorporates an adaptive potential field with the clothoid curve. First, we design an emergency triggering estimation to detect the ”blind alley” problem. Second, we regionalize the driving scene to search for the optimal breach point on the road PF and the final stopping point for the vehicle by considering the motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Potential Field-based path planning methods are widely embraced in the context of autonomous vehicles due to their real-time efficiency and simplicity. While the potential field effectively enforces a rigid road boundary to keep the vehicle within the confines of the road, it can lead to the “blind alley” problem caused by local minima in specific high- speed scenarios, resulting in indecision, erratic behavior, or even accidents. Therefore, the objective of this research is to anticipate and address the aforementioned problem in order to proactively avoid potential collisions. We have also found that existing methods do not offer a root cause analysis or practical solutions for this issue, which limits the practicality of the potential field in handling complicated traffic situations. In this paper, we propose an Emergency-Stopping Path Planning (ESPP) approach that incorporates an adaptive potential field with the clothoid curve. First, we design an emergency triggering estimation to detect the ”blind alley” problem. Second, we regionalize the driving scene to search for the optimal breach point on the road PF and the final stopping point for the vehicle by considering the motion range of the obstacle. Finally, we use the optimized clothoid curve to fit these calculated points under vehicle dynamics constraints to generate a smooth emergency avoidance path. The proposed ESPP method was evaluated by conducting the co-simulation between MATLAB/Simulink and CarSim Simulator in a freeway scene. The simulation results reveal that the proposed method shows increased performance in emergency collision avoidance and renders the vehicle safer, in which the duration of wheel slip is 61.9% shorter, and the maximum steering angle amplitude is 76.9% lower than other potential field-based methods.
Pengfei Lin, "Emergency-Aware Path Planning for Non-connected and Connected Autonomous Vehicles", Ph.D Thesis, The University of Tokyo, 2024.PhD Thesis | Abstract | BibTeX
@phdthesis{Lin2024,
title = {Emergency-Aware Path Planning for Non-connected and Connected Autonomous Vehicles},
author = {Pengfei Lin},
year = {2024},
date = {2024-03-31},
urldate = {2024-03-30},
school = {Ph.D Thesis, The University of Tokyo},
abstract = {Automotive safety is a paramount concern in the ever-evolving world of transportation. With millions of vehicles on the road, ensuring the well-being of drivers, passengers, and pedestrians is a critical priority. Specifically, traffic injuries remain a significant and ongoing threat to human properties, result- ing in a substantial number of casualties every year. Therefore, autonomous vehicles, also known as self-driving cars, are revolutionizing transportation by offering the potential to enhance road safety, reduce congestion, and increase mobility, while reshaping the future of urban and personal mobility.
An autonomous vehicle is equipped with advanced sensors and algorithms to navigate and operate without human intervention. However, despite years of research and development, autonomous vehicles continue to face safety challenges, with a series of traffic accidents still occurring during road tests. Three main challenges are summarized behind those tragedies: sensor failure that false detection or malfunctions lead to incorrect perception; software glitches that incorrect decision or planning paralysis leads to a fatal trajectory; communication Issues that fail to communicate with other road users.
Planning paralysis refers to a situation where the autonomous vehicle struggles to make decisions or take action due to complex, ambiguous, or unforeseen scenarios on the road. This can occur when the vehicle’s algorithms and sensors are overwhelmed by a multitude of factors, such as traffic emergencies, adverse weather conditions, or unfamiliar environments. Address- ing planning paralysis is a significant challenge, as it requires sophisticated decision-making capabilities and robust planning algorithms to ensure safe and efficient navigation in real-world conditions.
In this thesis, I present emergency-aware path planning for autonomous vehicles by incorporating optimization-based planning with two categories: non-connected and connected vehicles. The proposed planning module can stably generate a collision-free path under different traffic emergencies and can adapt to the collaboration intention of surrounding vehicles. This thesis work is composed of three steps that address the planning malfunctions in facing emergencies: (i) construct the risk map from the current traffic by potential field (PF); (ii) plan a collision-free path based on the collaboration intention of surrounding vehicles; (iii) monitor the PF and prepare for emer- gency navigation in the control layer based on the model predictive control (MPC).
Besides, a significant problem for PF-based path planning is that there are four inherent limitations to stop the planner from generating a safe path. In specific traffic scenarios, those limitations can be triggered, leading to plan- ning paralysis. Therefore, monitoring and foreseeing the possible appearance of those limitations is necessary for ensuring driving safety. At the same time, if the planning paralysis becomes irreversible, it is imperative to implant an emergency navigation function in the control module. The experiments also show that our proposed path planning performs better in collision avoidance (stable path generation, curvature, safety, etc.) than previous methods.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Automotive safety is a paramount concern in the ever-evolving world of transportation. With millions of vehicles on the road, ensuring the well-being of drivers, passengers, and pedestrians is a critical priority. Specifically, traffic injuries remain a significant and ongoing threat to human properties, result- ing in a substantial number of casualties every year. Therefore, autonomous vehicles, also known as self-driving cars, are revolutionizing transportation by offering the potential to enhance road safety, reduce congestion, and increase mobility, while reshaping the future of urban and personal mobility.
An autonomous vehicle is equipped with advanced sensors and algorithms to navigate and operate without human intervention. However, despite years of research and development, autonomous vehicles continue to face safety challenges, with a series of traffic accidents still occurring during road tests. Three main challenges are summarized behind those tragedies: sensor failure that false detection or malfunctions lead to incorrect perception; software glitches that incorrect decision or planning paralysis leads to a fatal trajectory; communication Issues that fail to communicate with other road users.
Planning paralysis refers to a situation where the autonomous vehicle struggles to make decisions or take action due to complex, ambiguous, or unforeseen scenarios on the road. This can occur when the vehicle’s algorithms and sensors are overwhelmed by a multitude of factors, such as traffic emergencies, adverse weather conditions, or unfamiliar environments. Address- ing planning paralysis is a significant challenge, as it requires sophisticated decision-making capabilities and robust planning algorithms to ensure safe and efficient navigation in real-world conditions.
In this thesis, I present emergency-aware path planning for autonomous vehicles by incorporating optimization-based planning with two categories: non-connected and connected vehicles. The proposed planning module can stably generate a collision-free path under different traffic emergencies and can adapt to the collaboration intention of surrounding vehicles. This thesis work is composed of three steps that address the planning malfunctions in facing emergencies: (i) construct the risk map from the current traffic by potential field (PF); (ii) plan a collision-free path based on the collaboration intention of surrounding vehicles; (iii) monitor the PF and prepare for emer- gency navigation in the control layer based on the model predictive control (MPC).
Besides, a significant problem for PF-based path planning is that there are four inherent limitations to stop the planner from generating a safe path. In specific traffic scenarios, those limitations can be triggered, leading to plan- ning paralysis. Therefore, monitoring and foreseeing the possible appearance of those limitations is necessary for ensuring driving safety. At the same time, if the planning paralysis becomes irreversible, it is imperative to implant an emergency navigation function in the control module. The experiments also show that our proposed path planning performs better in collision avoidance (stable path generation, curvature, safety, etc.) than previous methods.
An autonomous vehicle is equipped with advanced sensors and algorithms to navigate and operate without human intervention. However, despite years of research and development, autonomous vehicles continue to face safety challenges, with a series of traffic accidents still occurring during road tests. Three main challenges are summarized behind those tragedies: sensor failure that false detection or malfunctions lead to incorrect perception; software glitches that incorrect decision or planning paralysis leads to a fatal trajectory; communication Issues that fail to communicate with other road users.
Planning paralysis refers to a situation where the autonomous vehicle struggles to make decisions or take action due to complex, ambiguous, or unforeseen scenarios on the road. This can occur when the vehicle’s algorithms and sensors are overwhelmed by a multitude of factors, such as traffic emergencies, adverse weather conditions, or unfamiliar environments. Address- ing planning paralysis is a significant challenge, as it requires sophisticated decision-making capabilities and robust planning algorithms to ensure safe and efficient navigation in real-world conditions.
In this thesis, I present emergency-aware path planning for autonomous vehicles by incorporating optimization-based planning with two categories: non-connected and connected vehicles. The proposed planning module can stably generate a collision-free path under different traffic emergencies and can adapt to the collaboration intention of surrounding vehicles. This thesis work is composed of three steps that address the planning malfunctions in facing emergencies: (i) construct the risk map from the current traffic by potential field (PF); (ii) plan a collision-free path based on the collaboration intention of surrounding vehicles; (iii) monitor the PF and prepare for emer- gency navigation in the control layer based on the model predictive control (MPC).
Besides, a significant problem for PF-based path planning is that there are four inherent limitations to stop the planner from generating a safe path. In specific traffic scenarios, those limitations can be triggered, leading to plan- ning paralysis. Therefore, monitoring and foreseeing the possible appearance of those limitations is necessary for ensuring driving safety. At the same time, if the planning paralysis becomes irreversible, it is imperative to implant an emergency navigation function in the control module. The experiments also show that our proposed path planning performs better in collision avoidance (stable path generation, curvature, safety, etc.) than previous methods.
2023
Ye Tao, Ehsan Javanmardi, Pengfei Lin, Yuze Jiang, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data", In: IEEE Access, vol. 11, pp. 142846-142861, 2023, ISSN: 2169-3536.Journal Article | Abstract | BibTeX | Links:
@article{Tao2023b,
title = {Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data},
author = {Ye Tao and Ehsan Javanmardi and Pengfei Lin and Yuze Jiang and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2312.07948},
doi = {10.1109/ACCESS.2023.3343405},
issn = {2169-3536},
year = {2023},
date = {2023-12-17},
urldate = {2023-12-17},
journal = {IEEE Access},
volume = {11},
pages = {142846-142861},
abstract = {Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.
Pengfei Lin, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles", In: 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2023c,
title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06987},
year = {2023},
date = {2023-10-16},
urldate = {2023-10-16},
booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},
abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.
Vishal Chauhan, Chia-Ming Chang, Ehsan Javanmardi, Jin Nakazato, Koki Toda, Pengfei Lin, Takeo Igarashi, Manabu Tsukada, "Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load", In: The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023), Aveiro, Portugal, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Chauhan2023,
title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},
year = {2023},
date = {2023-10-12},
urldate = {2023-10-12},
booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},
address = {Aveiro, Portugal},
abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001).
Vishal Chauhan, Chia-Ming Chang, Ehsan Javanmardi, Jin Nakazato, Pengfei Lin, Takeo Igarashi, Manabu Tsukada, "Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization", In: Electronics, vol. 12, no. 20, 2023, ISSN: 2079-9292.Journal Article | Abstract | BibTeX | Links:
@article{Chauhan2023c,
title = {Fostering Fuzzy Logic in Enhancing Pedestrian Safety: Harnessing Smart Pole Interaction Unit for Autonomous Vehicle-to-Pedestrian Communication and Decision Optimization},
author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},
url = {https://www.mdpi.com/2079-9292/12/20/4207},
doi = {10.3390/electronics12204207},
issn = {2079-9292},
year = {2023},
date = {2023-10-11},
urldate = {2023-10-11},
journal = {Electronics},
volume = {12},
number = {20},
abstract = {In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in “naked streets”. This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles’ real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors.
Pengfei Lin, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, "Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2023b,
title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},
author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
doi = {10.1109/ITSC57777.2023.10422621},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
series = {Bilbao, Bizkaia, Spain},
abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.
Pengfei Lin, Ehsan Javanmardi, Ye Tao, Vishal Chauhan, Jin Nakazato, Manabu Tsukada, "Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles", In: 2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023), Anchorage, Alaska, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2023,
title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
year = {2023},
date = {2023-06-04},
urldate = {2023-06-04},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},
address = {Anchorage, Alaska, USA},
abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
},
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pubstate = {published},
tppubtype = {inproceedings}
}
Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
Pengfei Lin, Ying Shuai Quan, Jin Ho Yang, Chung Choo Chung, Manabu Tsukada, "Safety Tunnel-Based Model Predictive Path-Planning Controller with Potential Functions for Emergency Navigation", In: IEEE Transactions on Intelligent Transportation Systems, vol. 24, iss. 4, pp. 3974 - 3985, 2023, ISSN: 1524-9050.Journal Article | Abstract | BibTeX | Links:
@article{Lin2022e,
title = {Safety Tunnel-Based Model Predictive Path-Planning Controller with Potential Functions for Emergency Navigation},
author = {Pengfei Lin and Ying Shuai Quan and Jin Ho Yang and Chung Choo Chung and Manabu Tsukada},
doi = {10.1109/TITS.2022.3229699},
issn = {1524-9050},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {24},
issue = {4},
pages = {3974 - 3985},
abstract = {The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.
Ye Tao, Yuze Jiang, Pengfei Lin, Manabu Tsukada, Hiroshi Esaki, "zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception", In: 2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Tao2023,
title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},
author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},
url = {http://arxiv.org/abs/2211.07875},
doi = {10.1109/CCNC51644.2023.10059601},
year = {2023},
date = {2023-01-08},
urldate = {2023-01-08},
booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Las Vegas, NV, USA},
abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
2022
Pengfei Lin, Manabu Tsukada, "Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve", In: The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), Helsinki, Finland, 2022.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2022b,
title = {Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve},
author = {Pengfei Lin and Manabu Tsukada},
url = {https://youtu.be/AhgptWUyzSc},
doi = {10.1109/VTC2022-Spring54318.2022.9860508},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
booktitle = {The 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)},
address = {Helsinki, Finland},
abstract = {Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Potential field (PF)-based path planning is reported to be highly efficient for autonomous vehicles because it performs risk-aware computation and has a simple structure. However, the inherent limitations of the PF make it vulnerable in some specific traffic scenarios, such as local minima and oscillations in close obstacles. Therefore, a hybrid path planning with the sigmoid curve has recently been presented to generate better trajectories than those generated by the PF for collision avoidance. However, it is time-consuming and less applicable in complex dynamic environments, especially in traffic emergencies. To address these limitations, we propose a cooperative hybrid path planning (CHPP) approach that involves collaboration with adjacent vehicles for emergency collision avoidance via V2V communication. Moreover, the responsibility-sensitive safety (RSS) model is introduced to enhance the PF and sigmoid curve for safe-critical and time-saving requirements. The effectiveness of the proposed CHPP method compared with the state-of-the-art methods is studied through simulation of both static and dynamic traffic emergency scenarios. The simulation results prove that the CHPP approach performs better in terms of computation time (0.02 s faster) and driving safety (avoiding collision) than other methods, which are more supportive for emergency cooperative driving.
Pengfei Lin, Manabu Tsukada, "Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles", In: 13th Asian Control Conference (ASCC) 2022, Jeju, Korea, 2022.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2022c,
title = {Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.23919/ASCC56756.2022.9828160},
year = {2022},
date = {2022-05-03},
urldate = {2022-05-03},
booktitle = {13th Asian Control Conference (ASCC) 2022},
address = {Jeju, Korea},
abstract = {Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Potential field (PF), as a risk assessment method, is proposed to enhance autonomous vehicles’ (AVs) safety in collision avoidance. However, current PF targets mainly standalone-mode AVs (SAVs) by evaluating their relative position and velocity. In addition, the risk energy of the PF is usually assigned an infinite value along the z-axis. Therefore, this study presents an adaptive potential field (APF) for connected autonomous vehicles (CAVs). Valuable information (heading angle, steering wheel angle, etc.) other than relative position and velocity is supplemented to PF. Furthermore, we separate the APF from the cost function of the model predictive controller (MPC) to compute the desired reference signals directly, saving more computation time. The proposed APF-MPC is co-simulated in a comparative driving scenario via MATLAB/Simulink and CarSim simulator compared with the latest PF-MPC method.
Pengfei Lin, Manabu Tsukada, "Model Predictive Path-Planning Controller with Potential Function for Emergency Collision Avoidance on Highway Driving", In: Robotics and Automation Letters (RA-L) with IEEE International Conference on Robotics and Automation (ICRA) option, vol. 7, iss. 2, pp. 4662-4669, 2022, ISBN: 2377-3766.Journal Article | Abstract | BibTeX | Links:
@article{Lin2022,
title = {Model Predictive Path-Planning Controller with Potential Function for Emergency Collision Avoidance on Highway Driving},
author = {Pengfei Lin and Manabu Tsukada},
doi = {10.1109/LRA.2022.3152693},
isbn = {2377-3766},
year = {2022},
date = {2022-04-22},
urldate = {2022-04-22},
journal = {Robotics and Automation Letters (RA-L) with IEEE International Conference on Robotics and Automation (ICRA) option},
volume = {7},
issue = {2},
pages = {4662-4669},
abstract = {Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.
