activity
20202026
most citedSafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

23 citations · 99 across the 12 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

cs.LG2022★ 19 cited

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

Mengdi Xu, Zuxin Liu, Peide Huang +4

A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} cons…

cs.AI2022★ 3 cited

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions

Chulin Xie, Zhong Cao, Yunhui Long +3

Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encou…

cs.RO2022★ 7 cited

Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults

Ayoosh Bansal, Hunmin Kim, Simon Yu +4

Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous con…

cs.RO2022★ 12 cited

Verifiable Obstacle Detection

Ayoosh Bansal, Hunmin Kim, Simon Yu +4

Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate f…

cs.LG2022★ 11 cited

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Wenhao Ding, Haohong Lin, Bo Li +1

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards…

cs.RO2022★ 23 cited

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

Chejian Xu, Wenhao Ding, Weijie Lyu +6

As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts. This ha…

cs.RO2022★ 9 cited

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Wenhao Ding, Chejian Xu, Mansur Arief +3

Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms.…