1 citations · 3 across the 12 of their papers we have counts for
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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…
PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior
Chuheng Wei, Ziye Qin, Siyan Li +8
Driving behavior varies across individuals, yet most existing driving datasets do not preserve driver identity or control for factors that may affect driving style. We introduce th…
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…
Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving
Jessica Echterhoff, An Yan, Kyungtae Han +3
Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context…