John Chrosniak — accepted AI-conference papers

2024

Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

John Chrosniak, Jingyun Ning, Madhur Behl

RA-L 2024

Autonomous racing is a critical research area for autonomous driving, presenting significant challenges in vehicle dynamics modeling, such as balancing model precision and computational efficiency at high speeds ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www

Cited by 33 Source ↗Scholar ↗

2023

RACECAR - The Dataset for High-Speed Autonomous Racing

Amar Kulkarni, John Chrosniak, Emory Ducote, Florian Sauerbeck, Andrew Saba, Utkarsh Chirimar +3

IROS 2023poster

This paper describes RACECAR, the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data was collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge (2021–2022) have contr…

Cited by 14 Source ↗code ↗Scholar ↗