Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material.

Risk-Aware Driving Scenario Analysis with Large Language Models

Large Language Models (LLMs) can capture nuanced contextual relationships, reasoning, and complex problem-solving. By leveraging their ability to process and interpret large-scale information, LLMs have shown potential to address domain-specific challenges, including those in autonomous driving systems. This paper proposes a novel framework that leverages LLMs for risk-aware analysis of generated driving scenarios. We hypothesize that LLMs can effectively evaluate whether driving scenarios generated by autonomous driving testing simulators are safety-critical. To validate this hypothesis, we conducted an empirical evaluation to assess the effectiveness of LLMs in performing this task. This framework will also provide feedback to generate the new safety-critical scenario by using adversarial method to modify existing non-critical scenarios and test their effectiveness in validating motion planning algorithms. Code and scenarios are available at: https://github.com/yuangao-tum/Riskaware-Scenario-analyse