1 citations · 1 across the 4 of their papers we have counts for
7 papers
On Learning Closed-Loop Probabilistic Multi-Agent Simulator
Juanwu Lu, Rohit Gupta, Ahmadreza Moradipari +3
The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Re…
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…
Scene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance
Kyungtae Han, Yitao Chen, Rohit Gupta +1
While autonomous driving technologies continue to advance, current Advanced Driver Assistance Systems (ADAS) remain limited in their ability to interpret scene context or engage wi…
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…
NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models
Sung-Yeon Park, Can Cui, Yunsheng Ma +4
Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to comprehend driving scenes remain…
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…