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20242026
most citedLLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.RO20261 cited

LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends

Can Cui, Yunsheng Ma, Sung-Yeon Park +14

With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving tech…

cs.CV2025

PDB-Eval: An Evaluation of Large Multimodal Models for Description and Explanation of Personalized Driving Behavior

Junda Wu, Jessica Echterhoff, Kyungtae Han +3

Understanding a driver's behavior and intentions is important for potential risk assessment and early accident prevention. Safety and driver assistance systems can be tailored to i…

cs.CV2025

PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior

Chuheng Wei, Ziye Qin, Siyan Li +7

Driving behavior is inherently personal, influenced by individual habits, decision-making styles, and physiological states. However, most existing datasets treat all drivers as hom…

cs.CV2024

Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving

Yunsheng Ma, Amr Abdelraouf, Rohit Gupta +2

Multimodal large language models (MLLMs) have demonstrated remarkable potential for enhancing scene understanding in autonomous driving systems through powerful logical reasoning c…

cs.AI2024

Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions

Ziye Qin, Siyan Li, Guoyuan Wu +4

Dilemma zones at signalized intersections present a commonly occurring but unsolved challenge for both drivers and traffic operators. Onsets of the yellow lights prompt varied resp…

cs.LG2024

KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections

Chuheng Wei, Guoyuan Wu, Matthew J. Barth +3

Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenge…