@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},

doi = {10.1109/IV64158.2025.11097672},

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},

doi = {10.1109/IV64158.2025.11097554},

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}

}