Huge improvements and advances in sensor technology, together with the increasing demand for safety and handling performances, pull research towards new control strategies. Robustness and promptness of sensors used for active control are key requisites. The main idea behind the research presented in this paper is to instrument the tire with appropriate sensors in order to estimate several contact parameters ranging from the kinematic conditions of the tire (the longitudinal slippage and the side slip angle [Fukada, Y., 1999, Slip-angle estimation for vehicle stability control. Vehicle System Dynamics, 32(4–5), 375–388.]) to its dynamic properties (the contact area shape and dimensions as well as the longitudinal, lateral and vertical loads [Cole, D.J. and Cebon, D., 1989, A capacitative strip sensor for measuring dynamic type forces. Proc. of the Second International Conference on Road Traffic Monitoring, London, 38–42; Cole, D.J. and Cebon, D., 1992, Performance and application of a capacitative strip tyre force sensor. Proc of IEE Conference on Road Traffic Monitoring, London, 123–132.]) and to the adhesion characteristics of the road (the surface roughness [Pasterkamp, W.R. and Pacejka, H.B., 1997, The tire as a sensor to estimate friction. Vehicle System Dynamics, 27, 409–422; Pohl, A., Steindl, R. and Reindl, L., 1999, The ‘intelligent tire’ utilizing passive SAW sensorsmeasurement of tire friction. IEEE Transactions on Instrumentation and Measurement, 48(6), 1041–1046; Ray, L.R., 1997, Nonlinear tire force estimation and road friction identification: Simulation and experiments. Automatics, 33(10), 1819–1833.]). Thus, the tire becomes a sensor of tire–road interaction. Clearly, no other measuring device may ever be more robust nor prompt, and anyway closer to the contact area. After a preliminary research in which the pros and cons of the various sensor technologies were taken into account, it was decided to put accelerometers inside the tire. These accelerometers were fixed to the liner and on-board analysis of the acquired data was performed to reduce the amount of data then sent via radio control to a central data processing unit. This unit, through post-elaboration and recombination of the reduced signals coming from the various accelerometers in the tire, estimates some of the above-described contact parameters. In the near future, these contact parameters coming from all four tires and eventually other signals from sensors placed on the vehicle may be used to develop innovative control strategies in order to increase vehicle performances as well as running safety.

Hybrid energy storage systems that combine lithium-ion batteries and supercapacitors are considered as an attractive solution to overcome the drawbacks of battery-only energy storage systems, such as high cost, low power density, and short cycle life, which hinder the popularity of electric vehicles. A properly sized hybrid energy storage system and an implementable real-time power management system are of great importance to achieve satisfactory driving mileage and batterycyclelife.However,dimensioningandpowermanagement problems are quite complicated and challenging in practice. To address these challenges, this work proposes a Bi-level multi-objective design and control framework with the nondominated sorting genetic algorithm-II and fuzzy logic control as key components, to obtain an optimal sized hybrid energy storage system and the corresponding optimal real-time power management system based on fuzzy logic control simultaneously. In particular, a vectorized fuzzy inference system is devised, which allows large-scale fuzzy logic controllers to run in parallel, thereby improving optimization efficiency. Pareto optimal results of different hybrid energy storage systems incorporating both optimal design and control parameters are obtained efficiently thanks to the vectorization. An example solution chosen from the Pareto front shows that the proposed method can achieve a competitive number of covered laps while improving the battery cycle life significantly.

A 4-independent wheel driving (4-IWD) electric vehicle has distinctive advantages with both enhanced dynamic and energy efficiency performances since this configuration provides more flexibilities from both the design and control aspects. However, it is difficult to achieve the optimal performances of a 4-IWD electric vehicle with conventional design and control approaches. This work is dedicated to investigating the vehicular optimal design and control approaches, with a 4-IWD electric race car aiming at minimizing the lap time on a given circuit as a case study. A 14-DOF vehicle model that can fully evaluate the influences of the unsprung mass is developed based on Lagrangian dynamics. The 14-DOF vehicle model implemented with the reprogrammed Magic Formula tire model and a timeefficient suspension model supports metric operations and parallel computing, which can dramatically improve the computational efficiency. The optimal design and control problems with design parameters of the motor, transmission, mass center, anti-roll bar and the suspension of the race car are successively formulated. The formulated problems are subsequently solved by directly transcribing the original problems into large scale nonlinear optimization problems based on trapezoidal approach. The influences of the mounting positions of the propulsion system, the mass and inertia of the unsprung masses, the anti-roll bars and suspensions on the lap time are analyzed and compared quantitatively for the first time. Some interesting findings that are different from the “already known facts” are presented.

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.

In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density~(GM-PHD) filter with an Unscented Kalman Filter~(UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. We employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit.

When dealing with electric vehicles, different powertrain layouts can be exploited. Among them, the most interesting one in terms of vehicle lateral dynamics is represented by the one with independent electric motors: two or four electric motors. This allows torque-vectoring control strategies to be applied for increasing vehicle lateral performance and stability. In this paper, a novel control strategy based on torque-vectoring is used to design a drifting control that helps the driver in controlling the vehicle in such a condition. Drift is a particular cornering condition in which high values of sideslip angle are obtained and maintained during the turn. The controller is applied to a rear-wheel drive race car prototype with two independent electric motors on the rear axle. The controller relies only on lateral acceleration, yaw rate, and vehicle speed measurement. This makes it independent from state estimators, which can affect its performance and robustness.