The introduction of active safety systems and advanced driver assistance systems has enhanced the control authority over the vehicle dynamics through specialized actuators, enabling, for instance, independent wheel torque control. During emergency situations, these systems step in to aid the driver by limiting vehicle response to a stable and controllable range of low longitudinal tire slips and slip angles. This approach makes vehicle behavior predictable and manageable for the average human driver; however, it is conservative in case of driving automation. In fact, past research has shown that exceeding the operational boundaries of conventional active safety systems enables trajectories that are otherwise unattainable. This paper presents a nonlinear model predictive controller (NMPC) for path tracking (PT), which integrates steering, front-to-total longitudinal tire force distribution, and direct yaw moment actuation, and can operate beyond the limit of handling, e.g., to induce drift, if this is beneficial to PT. Simulation results of emergency conditions in an intersection scenario highlight that the proposed solution provides significant safety improvements, when compared to the concurrent operation of PT algorithms and the current generation of vehicle stability controllers.

This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.

Mobile robotic systems serve as versatile platforms for diverse indoor applications, ranging from warehousing and manufacturing to test benches dedicated to evaluating automated driving (AD) functions. In AD systems, the path following (PF) layer is responsible for defining steering commands to follow the reference path. Recently explored approaches involve artificial intelligence-based methods, such as Deep Reinforcement Learning (DRL). Despite their promising performance, these controllers still suffer from time-consuming training phases and may experience performance degradation when deviating from training conditions. To address these challenges, this paper proposes novel DRL controllers addressing the simulation-to-reality gap in unknown scenarios by: (i) training via an expert demonstrator which also speed up the learning phase; and (ii) a weight adaptation strategy for the resulting neural network (NN) to strengthen controller robustness and enhance PF performance. In addition, an experimentally validated vehicle model is used for training the proposed DRL algorithm and as a model for a federated extended Kalman filter (FEKF) system employed for sensor fusion in vehicle localisation. The proposed DRL-based PF controllers are experimentally evaluated through key performance indicators across multiple maneuvers not considered during training, and it is shown that they outperform benchmarking model-based controllers from the literature. Note to Practitioners —This paper presents a comprehensive toolchain for controlling mobile robots, which includes: (i) a simple yet effective two-stage least-square approach for parameter identification of the longitudinal and lateral dynamics of scaled robotic vehicles; (ii) the utilisation of a no-reset FEKF to enhance positioning leveraging all sensors commonly available on scaled robotic vehicles; (iii) the inclusion of an expert demonstrator to expedite the training phase and address the simulation-to-reality gap resulting from discrepancies between simulation and experimental environments; and (iv) an adaptation strategy for dynamically adjusting the weights of the resulting NN to further improve robustness for scenarios not considered during the traning.

In recent years, the automotive industry has undergone a rapid evolution, with increasing interest in electric vehicles (EVs). In this context, improving driving comfort (beyond the reduction of internal-combustion-engine related vibrations) and vehicle safety have become crucial factors for the widespread adoption of EVs. This can be achieved thank to the easier modulation of the individual propulsion torques on both axles, which can be exploited for enhancing stability/agility, but also to improve the reaction of the vehicle to external factors, such as road asperities. In particular, longitudinal vehicle vibrations, although often overlooked in comparison to vertical vibrations, have a significant impact on driving comfort. This paper devises a Reinforcement Learning (RL) torque-correction controller aimed at reducing longitudinal vibrations. The proposed controller, based on the Deep Deterministic Policy Gradient (DDPG) algorithm, has been implemented on a validated vehicle model for both the front and rear axles, modulating the torque requested by the driver in response to road unevenness or obstacles. The agent was trained/tested in simulations on various road scenarios, including a variety of obstacles as well as real road profiles sampled experimentally. The results show that the proposed RL controller offers performance comparable to that of a state-of-the-art Nonlinear Model Predictive Controller (NMPC) in terms of vibration reduction, while maintaining a much lower computational load, making it potentially suitable for real-time applications in road vehicles equipped with production automotive-graded ECUs. In this proof-of-concept study, the controller was implemented on a dSPACE MicroAutoBox III device, showing a turnaround time < 1 ms compared with the 6−7 ms required by the considered real-time capable NMPC, on the same unit. Robustness tests were also carried out to assess the controller’s response against different road conditions, road detection accuracy and vehicle parameters, confirming the validity of the proposed approach.