4 papers · 1 filter
Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search
Shuo Yang, Caojun Wang, Yuanjian Zhang +4
Adversarial scenario generation is crucial for autonomous driving testing because it can efficiently simulate various challenge and complex traffic conditions. However, it is diffi…
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
Shuo Yang, Shizhen Li, Yanjun Huang +1
Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios.…
A Safety-Oriented Self-Learning Algorithm for Autonomous Driving: Evolution Starting from a Basic Model
Shuo Yang, Caojun Wang, Zhenyu Ma +2
Autonomous driving vehicles with self-learning capabilities are expected to evolve in complex environments to improve their ability to cope with different scenarios. However, most…
A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
Shuo Yang, Liwen Wang, Yanjun Huang +1
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforceme…