@inproceedings{Li2025b,

title = {State-Guided Spatial Cross-Attention for Enhanced End-to-End Autonomous Driving},

author = {Dongyang Li and Ehsan Javanmardi and Manabu Tsukada},

year  = {2025},

date = {2025-09-30},

urldate = {2025-09-30},

booktitle = {IEEE International Automated Vehicle Validation Conference (IAVVC 2025)},

address = {Baden-Baden, Germany},

abstract = {Handling near-accident scenarios is a significant challenge for end-to-end autonomous driving (E2E-AD), as these situations often involve sudden environmental changes, complex interactions with other road users, and high-risk decision-making under uncertainty. Unlike routine driving tasks, near-accident scenarios require rapid and precise responses based on external perception and internal vehicle dynamics. Successfully navigating such situations demands not only a comprehensive understanding of the surrounding environment but also an accurate assessment of the ego vehicle's state, including speed, acceleration, and steering angle, to ensure safe and reliable control. However, conventional E2E-AD models struggle to handle these safety-critical situations effectively. Standard approaches primarily rely on raw sensor inputs to learn driving policies, often overlooking the crucial role of vehicle state information in decision-making. Since many near-accident scenarios involve conditions where the same environmental observation could require vastly different responses depending on the ego vehicle's motion state-such as whether the vehicle is braking, accelerating, or experiencing traction loss-ignoring these internal dynamics can lead to unsafe or suboptimal actions. Furthermore, E2E-AD models typically learn a direct mapping from sensory inputs to control outputs, making it difficult to generalize to highly dynamic and unpredictable interactions, such as emergency evasive maneuvers or sudden braking events. To address these challenges, we propose a state-guided cross-attention mechanism that explicitly models the interaction between the ego vehicle's states and its perception of the environment. By incorporating vehicle state information into the decision-making process, our approach ensures that the model can dynamically adjust its attention to critical sensory inputs based on real-time driving conditions. This allows the autonomous system to make more context-aware decisions, improving its ability to respond effectively to complex and safety-critical scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Li2025c,

title = {PrefDrive: Enhancing Autonomous Driving through Preference-Guided Large Language Models},

author = {Yun Li and Ehsan Javanmardi and Simon Thompson and Kai Katsumata and Alex Orsholits and Manabu Tsukada},

url = {https://github.com/LiYun0607/PrefDrive/

https://huggingface.co/liyun0607/PrefDrive

https://huggingface.co/datasets/liyun0607/PrefDrive},

year  = {2025},

date = {2025-06-22},

urldate = {2025-06-22},

booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},

address = {Cluj-Napoca, Romania},

abstract = {This paper presents PrefDrive, a novel framework that integrates driving preferences into autonomous driving models through large language models (LLMs). While recent advances in LLMs have shown promise in autonomous driving, existing approaches often struggle to align with specific driving behaviors (e.g., maintaining safe distances, smooth acceleration patterns) and operational requirements (e.g., traffic rule compliance, route adherence). We address this challenge by developing a preference learning framework that combines multimodal perception with natural language understanding. Our approach leverages Direct Preference Optimization (DPO) to fine-tune LLMs efficiently on consumer-grade hardware, making advanced autonomous driving research more accessible to the broader research community. We introduce a comprehensive dataset of 74,040 sequences, carefully annotated with driving preferences and driving decisions, which, along with our trained model checkpoints, will be made publicly available to facilitate future research. Through extensive experiments in the CARLA simulator, we demonstrate that our preference-guided approach significantly improves driving performance across multiple metrics, including distance maintenance and trajectory smoothness. Results show up to 28.1% reduction in traffic rule violations and 8.5% improvement in navigation task completion while maintaining appropriate distances from obstacles. The framework demonstrates robust performance across different urban environments, showcasing the effectiveness of preference learning in autonomous driving applications.  },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiang2025,

title = {Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility},

author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2504.06772},

year  = {2025},

date = {2025-06-22},

urldate = {2025-06-22},

booktitle = {36th IEEE Intelligent Vehicles Symposium (IV2025)},

address = {Cluj-Napoca, Romania},

abstract = {The deployment of roadside LiDAR sensors plays a crucial

role in the development of Cooperative Intelligent

Transport Systems (C-ITS). However, the high cost of LiDAR

sensors necessitates efficient placement strategies to

maximize detection performance. Traditional roadside LiDAR

deployment methods rely on expert insight, making them

time-consuming.  Automating this process, however, demands

extensive computation, as it requires not only visibility

evaluation but also assessing detection performance across

different LiDAR placements. To address this challenge, we

propose a fast surrogate metric, the Entropy-Guided

Visibility Score (EGVS), based on information gain to

evaluate object detection performance in roadside LiDAR

configurations. EGVS leverages Traffic Probabilistic

Occupancy Grids (TPOG) to prioritize critical areas and

employs entropy-based calculations to quantify the

information captured by LiDAR beams. This eliminates the

need for direct detection performance evaluation, which

typically requires extensive labeling and computational

resources. By integrating EGVS into the optimization

process, we significantly accelerate the search for optimal

LiDAR configurations. Experimental results using the AWSIM

simulator demonstrate that EGVS strongly correlates with

Average Precision (AP) scores and effectively predicts

object detection performance. This approach offers a

computationally efficient solution for roadside LiDAR

deployment, facilitating scalable smart infrastructure

development. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}