2 citations · 2 across the 9 of their papers we have counts for
10 papers
Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning
Yitao Xu, Tong Wu, Yiyan Wu +7
Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X benchmarks provide limited s…
RISE: Roadside Infrastructure Sequence Understanding across 3D Tracking and Structured Vision-Language Reasoning
Yanbo Jiang, Haotian Zheng, Jiahao Wang +10
We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequen…
Vault: One-Step Latent Generation with Positive-Anchored Rewards for Autonomous Driving
Yining Xing, Zehong Ke, Zhiyuan Liu +3
End-to-end autonomous driving must balance multimodal maneuver generation against real-time inference constraints. Diffusion planners capture diverse behaviors, but their iterative…
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…
DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving
Zhiye Wang, Yanbo Jiang, Rui Zhou +5
Large language models (LLMs) have shown great promise for autonomous driving. However, discretizing numbers into tokens limits precise numerical reasoning, fails to reflect the pos…
Modified-Emergency Index (MEI): A Criticality Metric for Autonomous Driving in Lateral Conflict
Hao Cheng, Yanbo Jiang, Qingyuan Shi +5
Effective, reliable, and efficient evaluation of autonomous driving safety is essential to demonstrate its trustworthiness. Criticality metrics provide an objective means of assess…
S2O: An Integrated Driving Decision-making Performance Evaluation Method Bridging Subjective Feeling to Objective Evaluation
Yuning Wang, Zehong Ke, Yanbo Jiang +4
Autonomous driving decision-making is one of the critical modules towards intelligent transportation systems, and how to evaluate the driving performance comprehensively and precis…
D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation
Zehong Ke, Yanbo Jiang, Yuning Wang +3
With the advancement of deep learning technology, data-driven methods are increasingly used in the decision-making of autonomous driving, and the quality of datasets greatly influe…
CAME: Contrastive Automated Model Evaluation
Ru Peng, Qiuyang Duan, Haobo Wang +5
The Automated Model Evaluation (AutoEval) framework entertains the possibility of evaluating a trained machine learning model without resorting to a labeled testing set. Despite th…