6 citations · 18 across the 37 of their papers we have counts for
9 papers · 2 filters
Are All Data Necessary? Efficient Data Pruning for Large-scale Autonomous Driving Dataset via Trajectory Entropy Maximization
Zhaoyang Liu, Weitao Zhou, Junze Wen +4
Collecting large-scale naturalistic driving data is essential for training robust autonomous driving planners. However, real-world datasets often contain a substantial amount of re…
DTCCL: Disengagement-Triggered Contrastive Continual Learning for Autonomous Bus Planners
Yanding Yang, Weitao Zhou, Jinhai Wang +8
Autonomous buses run on fixed routes but must operate in open, dynamic urban environments. Disengagement events on these routes are often geographically concentrated and typically…
MTRDrive: Memory-Tool Synergistic Reasoning for Robust Autonomous Driving in Corner Cases
Ziang Luo, Kangan Qian, Jiahua Wang +13
Vision-Language Models(VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the rel…
DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
Weitao Zhou, Bo Zhang, Zhong Cao +5
With the increasing presence of automated vehicles on open roads under driver supervision, disengagement cases are becoming more prevalent. While some data-driven planning systems…
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving
Kangan Qian, Sicong Jiang, Yang Zhong +18
Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate…
FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback
Kangan Qian, Ziang Luo, Sicong Jiang +16
Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems. While end-to-end models trained on large datasets perform well in standard driving sce…
RAVE: End-to-end Hierarchical Visual Localization with Rasterized and Vectorized HD map
Jinyu Miao, Tuopu Wen, Kun Jiang +8
Accurate localization serves as an important component in autonomous driving systems. Traditional rule-based localization involves many standalone modules, which is theoretically f…
Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving
Nanshan Deng, Weitao Zhou, Bo Zhang +4
Current autonomous vehicles operate primarily within limited regions, but there is increasing demand for broader applications. However, as models scale, their limited capacity beco…
Residual Learning towards High-fidelity Vehicle Dynamics Modeling with Transformer
Jinyu Miao, Rujun Yan, Bowei Zhang +6
The vehicle dynamics model serves as a vital component of autonomous driving systems, as it describes the temporal changes in vehicle state. In a long period, researchers have made…