Wheel slip control for ground vehicles with individually controlled electric motors can be realized with strategies that can significantly differ from the conventional antilock braking system (ABS) and traction control (TC) system. This paper provides a review of state-of-the-art technology and recent developments in TC and ABSs using the actuation of electric motors. Particular attention is paid to the realization of slip estimators, the formalization of torque demand, and the control methods applied for the implementation of TC and ABSs. The performed analysis allowed for the differentiation of several most elaborated methods for slip and torque control and defining still imperfectly investigated problems to be covered by the further development of TC and ABSs for full electric vehicles.

A prime concern for automated vehicles is motion comfort, as an uncomfortable ride may reduce acceptance of the technology amongst the general population. However, it is not clear how transient motions typical for travelling by car affect the experience of comfort. Here, we determine the relation between properties of vehicle motions (i.e., acceleration and jerk) and discomfort empirically, and we evaluate the ability of normative models to account for the data. 23 participants were placed in a moving-base driving simulator and presented sinusoidial and triangular motion pulses with various peak accelerations (A_max0.4 - 2 ms^-2) and jerks (J_max0.5 - 15 ms^-3), designed to recreate typical vehicle accelerations. Participants provided discomfort judgments on absolute 'Verbal Qualifiers' and relative 'Magnitude Estimates' associated with these motions. The data show that discomfort increases with acceleration amplitude, and that the strength of this effect depends on the direction of motion. We furthermore find that higher jerks (shorter duration pulses) are considered more comfortable, and that triangular pulses are more comfortable than sinusoidal pulses. ME responses decrease (i.e., reduced discomfort) with increasing pulse duration. Evaluations of normative models of vibration and shock (ISO 2631), and perceived motion intensity provide mixed results. The vibration model could not account for the data well. Reasonable agreement between predictions and observations were found for the shock model and perceived intensity model, which emphasize the role of acceleration. We present novel statistical models that describe motion comfort as a function of acceleration, jerk, and direction. The present findings are essential to develop motion planning algorithms aimed at maximizing comfort.

Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota’s high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark.

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

A novel strategy to enhance string stability of autonomous vehicles with sensor delay and actuator lag is proposed based on a model predictive control framework. To compensate sensor delay, the approach entails estimating the (unknown) system state at the current time using the system state in a previous time, the applied control history and a system dynamics model. The actuator lag is compensated by including the lag in the state prediction model. The mathematical framework shows that without the anticipation strategy, sensor delay leads to a worse estimate of the initial condition for the optimal control problem and actuator lag increases the mismatch between the system state prediction model and the actual system behaviour. Simulation verified that sensor delay and actuator lag degrade string stability of platoons. The proposed anticipatory control strategy shows clear benefits in improving autonomous vehicle string stability and hence has potential to enhance traffic flow stability.

This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on a non-linear single-track vehicle model to enhance the estimation accuracy of a Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, and both use the standard inertial measurement unit and the tyre forces measured by load sensing technology. CNN benefits from the UKF the capacity to leverage the laws of physics. Concurrently, the UKF uses the CNN outputs as sideslip angle pseudo-measurement and adaptive process noise parameters. The back-propagation through time algorithm is applied end-to-end to the CNN and the UKF to employ the mutualistic property. Using a large-scale experimental dataset of 216 manoeuvres containing a great diversity of vehicle behaviours, we demonstrate a significant improvement in the accuracy of the proposed architecture over the current state-of-art hybrid approach combined with model-based and data-driven techniques. In the case that a limited dataset is provided for the training phase, the proposed hybrid approach still guarantees estimation robustness.