This paper presents a hierarchical framework with novel analytical and neural physics-driven models, to enable the online planning and tracking of minimum-time maneuvers, for a vehicle with partially-unknown parameters. We introduce a lateral speed prediction model for high-level motion planning with economic nonlinear model predictive control (E-NMPC). A low-level steering controller is developed with a novel feedforward-feedback physics-driven artificial neural network (NN). A longitudinal dynamic model is identified to tune a low-level speed-tracking controller. The high- and low-level control models are identified with an automatic three-step scheme, combining open-loop and closed-loop maneuvers to model the maximum acceleration G-G-v performance constraint for E-NMPC, and to capture the effect of the longitudinal acceleration on the lateral dynamics. The proposed framework is used in a simulation environment, for the online closed-loop control of a highly detailed sedan vehicle simulator, whose parameters are partially-unknown. Two different circuits are adopted to validate the approach, and a robustness analysis is performed by varying the vehicle mass and the load distribution. A minimum-time optimal control problem is solved offline and used for a comparison with the closed-loop results. A video demonstrating both the automatic three-step identification scheme and the motion planning and control results is available at the following link: https://www.youtube.com/watch?v=xQ_T96IjGP8.

This paper introduces a novel non-smooth numerical optimization approach for solving the Three-Point Dubins Problem (3PDP). The 3PDP requires determining the shortest path of bounded curvature that connects given initial and final positions and orientations while traversing a specified waypoint. The inherent discontinuity of this problem precludes the use of conventional optimization algorithms. We propose two innovative methods specifically designed to address this challenge. These methods not only effectively solve the 3PDP but also offer significant computational efficiency improvements over existing state-of-the-art techniques. Our contributions include the formulation of these new algorithms, a detailed analysis of their theoretical foundations, and their implementation. Additionally, we provide a thorough comparison with current leading approaches, demonstrating the superior performance of our methods in terms of accuracy and computational speed. This work advances the field of path planning in robotics, providing practical solutions for applications requiring efficient and precise motion planning.

The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at high computational cost or use conservative box constraints for computational efficiency. We propose FBGA, a new Forward-Backward algorithm with Generic Acceleration constraints, which achieves both high accuracy and low computation time. FBGA operates forward and backward passes to maximize the velocity profile in short, discretized path segments, while satisfying user-defined performance limits. Tested on five racetracks and two vehicle classes, FBGA handles complex, non-convex acceleration constraints with custom formulations. Its maneuvers and lap times closely match optimal control baselines (within 0.11%-0.36%), while being up to three orders of magnitude faster. FBGA maintains high accuracy even with coarse discretization, making it well-suited for online multi-query trajectory planning.

Highly automated vehicles are complex systems, and ensuring their safe operation within their Operational Design Domain (ODD) presents significant challenges. Diagnosing failure modes and updating these systems are even more demanding tasks. This paper introduces a method to assist with assessing, diagnosing, and updating these systems by developing a stochastic model that predicts safety outcomes (collision, near-miss, or safe state) with quantified uncertainty in any parametrized scenario. The approach uses bootstrapping aggregation to create an ensemble of predictive models, leveraging fully connected feed-forward neural networks. These networks are designed with a flexible number of trainable parameters and hidden layers, requiring minimal computational resources. The model is trained on a small set of examples obtained through direct simulations that randomly sample the parametric scenario, bypassing the traditional test matrix definition. Once trained, the bootstrapped model serves as an identity card for the system under test, allowing continuous performance evaluation across the parametric scenario. The paper demonstrates applications, including safety assessment, failure mode identification, and developing a safe speed recommendation function. The model's compact size ensures rapid execution, facilitating extensive post-analysis for safety argumentation and diagnosis and real-time online use to extend the system's abilities.