Automobiles are becoming more and more complex as multiple control systems are integrated into the vehicle platform. This paper investigates the coordination of active rear steering (RWS) and torque vectoring (TV) – which is enabled by independent electric motors at the rear axle – in controlling vehicle lateral dynamics. The proposed controller aims at enhancing vehicle handling performance and stability while cornering. The coordination of the two actuators is achieved by weighting their contribution based on their impact on vehicle dynamics according to the working condition. The impact of each control system is assessed by means of phase portraits. These plots are a very powerful tool for analysing vehicle nonlinear dynamics as they readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and control inputs. Based on phase portrait analysis, a performance index is thus proposed, which weights more the control action (TV or RWS) capable of leading the vehicle at the nearest equilibrium point with the fastest rate. The controller performance is assessed through numerical simulations carried out using a nonlinear 14 dofs vehicle model. Results are compared with ones of the two controllers alone (RWS and TV) in different manoeuvers and adherence conditions.

Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.

Teleoperation provides the human operator with sophisticated perceptual and cognitive skills in an over-the-network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a fallback plan. Variable network time-delay in data transmission is the major problem in teleoperating a vehicle. On 4G network, variability of this delay is significant (70-150 ms ping). Due to this, both video streaming and driving commands encounter variable time-delay. This paper presents an approach to provide the human-operator with a forecasted video stream that replicates future perspective of vehicle’s field of view accounting for the delay present in the network. Regarding the image transformation, perspective projection technique is combined with correction given by Smith predictor in the control loop. This image transformation accounts current time-delay and tries to address both issues, time-delays as well as variability. For experiment sake, only frontward field of view is forecasted. Performance is evaluated by performing vehicle teleoperation with real vehicle on street edge-case maneuvers and later comparing the cross-track error with and without perspective projection. Results obtained show improvement in path following tasks.

The vehicle sideslip angle or lateral velocity is a measure both for driving stability and for occupant’s subjective perception of safety. With the introduction of vehicle dynamics control systems and automated driving functions, knowledge of this vehicle motion state is required for many control strategies. This article gives an overview on the state of the art on sideslip angle estimation. In contrast to other literature studies on this topic, it focuses on vehicle dynamics based algorithms. The following types of observers are discussed: Kalman Filter-type, recursive least squares (RLS), sliding mode observers (SMO) or nonlinear observers (NLO). Eventually, cascaded observers are used that first estimate some states, which then act as input to the sideslip angle estimator. Since the choice of an observer strategy always depends on the application, this article provides a brief insight into the work of selected research groups that have studied the topic. These examples will help to clarify the presence of many different approaches in the literature. A detailed discussion on vehicle and tire models is not included but referenced to other sources. Finally, this article provides recommendations for two main target groups: First, researchers and engineers that plan to design an algorithm for sideslip angle estimation using deterministic vehicle dynamics based approaches. Second, researchers and engineers planning to include an existing algorithm in an automated driving function that want to learn about advantages and limitations of these types of algorithms.

Electric motors offer the possibility to control the braking torque in a more precise way than the hydraulic circuit. The applied torque is in fact measured and so it can be used to feedback the controller. Moreover, this information can be used to estimate the tyre–road friction coefficient, before potential friction is reached. The present paper proposes a novel ABS control strategy for electric vehicles with distributed motors. The controller is a six states machine that uses the information on applied braking torque provided by the electric motors. This information is of paramount importance to understand when the wheel reaches the peak of the braking force. Simulation results shows the benefits of the proposed strategy, comparing the results with state of the art acceleration-based controller.