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.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
This paper introduces a minimalistic autonomous racing stack designed for high-speed time-trial racing, emphasizing rapid deployment and efficient system integration with minimal on-track testing. Validated on real speedways, the stack achieved a top speed of 206 km/h within just 11 hours of practice, covering 325 km. The system performance analysis includes tracking accuracy, vehicle dynamics, and safety considerations. Why it matters: This research offers insights for teams aiming to quickly develop and deploy autonomous racing stacks with limited track access, potentially accelerating innovation in autonomous vehicle technology within the A2RL and similar racing initiatives.
Team TII EuroRacing (TII-ER) developed a full autonomous software stack for oval racing, enabling speeds above 75 m/s (270 km/h). The software includes modules for perception, planning, control, vehicle dynamics modeling, simulation, telemetry, and safety. The team achieved second and third place in the first two Indy Autonomous Challenge events using this stack.
The paper introduces Yet another Policy Optimization (YaPO), a reference-free method for learning sparse steering vectors in the latent space of a Sparse Autoencoder (SAE) to steer LLMs. By optimizing sparse codes, YaPO produces disentangled, interpretable, and efficient steering directions. Experiments show YaPO converges faster, achieves stronger performance, exhibits improved training stability and preserves general knowledge compared to dense steering baselines.