1 citations · 1 across the 6 of their papers we have counts for
6 papers
A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
Heye Huang, Jingguang Li, Zhiyuan Zhou +4
Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety. These scenarios are…
From Scene to Object: Text-Guided Dual-Gaze Prediction
Zehong Ke, Yanbo Jiang, Jinhao Li +5
Interpretable driver attention prediction is crucial for human-like autonomous driving. However, existing datasets provide only scene-level global gaze rather than fine-grained obj…
Driving risk emerges from the required two-dimensional joint evasive acceleration
Hao Cheng, Yanbo Jiang, Wenhao Yu +9
Most autonomous driving safety benchmarks use time-to-collision (TTC) to assess risk and guide safe behaviour. However, TTC-based methods treat risk as a one-dimensional closing pr…
CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy
Heye Huang, Yibin Yang, Mingfeng Fan +3
Safe autonomous driving in mixed traffic requires a unified understanding of multimodal interactions and dynamic planning under uncertainty. Existing learning based approaches stru…
CSDO: Enhancing Efficiency and Success in Large-Scale Multi-Vehicle Trajectory Planning
Yibin Yang, Shaobing Xu, Xintao Yan +3
This paper presents an efficient algorithm, naming Centralized Searching and Decentralized Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory P…
Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments
Heye Huang, Jinxin Liu, Guanya Shi +3
This paper proposes an adaptive behavioral decision-making method for autonomous vehicles (AVs) focusing on complex merging scenarios. Leveraging principles from non-cooperative ga…