5 papers · 1 filter
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2
Reliable confidence estimates are important for safely deploying vision-based controllers in autonomous racing, where safety predictions must be derived from camera images, yet mod…
TEACar: An Open-Source Autonomous Driving Platform
Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes +5
Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardwa…
How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Autonomous robots that rely on deep neural network controllers pose critical challenges for safety prediction, especially under partial observability and distribution shift. Tradit…
Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing
Christopher Oeltjen, Carson Sobolewski, Saleh Faghfoorian +4
Accurate knowledge of the tire-road friction coefficient (TRFC) is essential for vehicle safety, stability, and performance, especially in autonomous racing, where vehicles often o…
Generalizable Image Repair for Robust Visual Control
Carson Sobolewski, Zhenjiang Mao, Kshitij Maruti Vejre +1
Vision-based control relies on accurate perception to achieve robustness. However, image distribution changes caused by sensor noise, adverse weather, and dynamic lighting can degr…