4 papers
Driving Intents Amplify Planning-Oriented Reinforcement Learning
Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang +4
Continuous-action policies trained on a single demonstrated trajectory per scene suffer from mode collapse: samples cluster around the demonstrated maneuver and the policy cannot r…
Action Emergence from Streaming Intent
Pengfei Jing, Victor Shea-Jay Huang, Hengtong Lu +3
We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appropriate, and safety-compliant…
Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluation
Yangyang Liu, Lei Xue, Sishan Wang +6
The progress of automotive technologies has made cybersecurity a crucial focus, leading to various cyber attacks. These attacks primarily target the Controller Area Network (CAN) a…
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity
Pengfei Jing, Mengyun Tang, Xiaorong Shi +5
Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code…