Researchers propose MS-NN-steer, a model-structured neural network for autonomous vehicle steering control that integrates nonlinear vehicle dynamics. The controller was validated using real-world data from the Abu Dhabi Autonomous Racing League (A2RL) competition. MS-NN-steer demonstrates improved accuracy, generalization, and robustness compared to general-purpose NNs and the A2RL winning team's controller. Why it matters: This research demonstrates a promising approach to developing transparent and reliable AI for safety-critical autonomous racing applications in the UAE.
This paper introduces a longitudinal control system for autonomous racing vehicles with combustion engines, translating trajectory-tracking commands into low-level vehicle controls like throttle, brake pressure, and gear selection. The modular design facilitates integration with various trajectory-tracking algorithms and vehicles. Experimental validation on the EAV24 racecar during the Abu Dhabi Autonomous Racing League at Yas Marina Circuit demonstrated the system's effectiveness, achieving longitudinal accelerations up to 25 m/s². Why it matters: This research contributes to the advancement of autonomous racing technology in the region, showcasing practical applications in high-performance scenarios and fostering innovation in vehicle control systems.