6 papers
How defensive driving enhances driving safety: A driving simulator study on drivers' defensive driving behaviors
Xinzheng Wu, Junyi Chen, Shaolingfeng Ye +1
Defensive driving is widely recognized as an advanced driving skill. However, whether and how defensive driving affects driving safety remains insufficiently investigated. This stu…
VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion
Xinzheng Wu, Junyi Chen, Naiting Zhong +1
The safe deployment of autonomous driving systems (ADSs) relies on comprehensive testing and evaluation. However, safety-critical scenarios that can effectively expose system vulne…
An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learning
Xinzheng Wu, Junyi Chen, Shaolingfeng Ye +2
In the autonomous driving testing methods based on evolving scenarios, the construction method of the driver model, which determines the driving maneuvers of background vehicles (B…
RISEE: A Highly Interactive Naturalistic Driving Trajectories Dataset with Human Subjective Risk Perception and Eye-tracking Information
Xinzheng Wu, Junyi Chen, Peiyi Wang +3
In the research and development (R&D) and verification and validation (V&V) phases of autonomous driving decision-making and planning systems, it is necessary to integrate human fa…
Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems
Xinzheng Wu, Junyi Chen, Jianfeng Wu +3
Testing and evaluation is an important step before the large-scale application of the autonomous driving systems (ADSs). Based on the three level of scenario abstraction theory, a…
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
Xinzheng Wu, Junyi Chen, Xingyu Xing +4
Scenario-based virtual testing is one of the most significant methods to test and evaluate the safety of automated driving systems (ADSs). However, it is impractical to enumerate a…