2 citations · 4 across the 6 of their papers we have counts for
8 papers
Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks
Benjamin David Evans, Raphael Trumpp, Marco Caccamo +4
The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications…
High-performance Racing on Unmapped Tracks using Local Maps
Benjamin David Evans, Hendrik Willem Jordaan, Herman Arnold Engelbrecht
Map-based methods for autonomous racing estimate the vehicle's location, which is used to follow a high-level plan. While map-based optimisation methods demonstrate high-performanc…
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
Andrew Murdoch, Johannes Cornelius Schoeman, Hendrik Willem Jordaan
In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practic…
High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning
Benjamin David Evans, Herman Arnold Engelbrecht, Hendrik Willem Jordaan
The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can tr…
Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor
Benjamin David Evans, Johannes Betz, Hongrui Zheng +3
Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the rob…
Reward Signal Design for Autonomous Racing
Benjamin Evans, Herman A. Engelbrecht, Hendrik W. Jordaan
Reinforcement learning (RL) has shown to be a valuable tool in training neural networks for autonomous motion planning. The application of RL to a specific problem is dependent on…