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In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance. [ABSTRACT FROM AUTHOR]

This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time. [ABSTRACT FROM AUTHOR]

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluate motion sickness. The trade-off between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the effect of driving style, vehicle speed, and road width, which can be considered among the main factors affecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The effect of the road width is negligible on both sickness and journey time when travelling on a longer road. The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for resolution of traffic conflicts occurring on an intersection, intended for Connected Autonomous Vehicle (CAV). The algorithm is based on the trade-off between energy consumption and user-defined value of time. The consequent cooperation opportunities originating from agent heterogeneity are captured by a game-theoretic cooperative-competitive solution approach to develop a computationally feasible, self-enforced, cooperative intersection de-conflicting algorithm. It is intended as a component of a robust framework for strategic control of the vehicle’s powertrain. Monte Carlo simulation is used to showcase the decision-making algorithm’s behaviour, to estimate its efficiency as a function of traffic heterogeneity. The results confirm that the proposed algorithm may offer threefold improvement in energy and time efficiency in relation to a First Come First Served scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a decision-making framework for Connected Autonomous Vehicle interactions. It provides and justifies algorithms for strategic selection of control references for cruising, platooning and overtaking. The algorithm is based on the trade-off between energy consumption and time. The consequent cooperation opportunities originating from agent heterogeneity are captured by a game-theoretic cooperative-competitive solution concept to provide a computationally feasible, self-enforced, cooperative traffic management framework. [ABSTRACT FROM AUTHOR]

In this work, a novel distribution-based control strategy of semi-active vehicle suspensions is tested under different conditions. The novelty lies in the use of an appropriate threshold in the operational condition of the control algorithm, with which the operational conditions severity is quantified and the state of the damper is controlled according to the magnitude of the operational conditions and not their sign. The value of the threshold depends on the vibrations induced to the sprung mass by the road profile. In order to be evaluated, the operational conditions of the algorithm are fitted to a t -student distribution. The cumulative distribution function of this distribution is used in order to decrease the fraction of the sample operating with the damper's stiff state. The strategy is applied to traditional SH control algorithms and is tested using a quarter car model excited by different road excitations. A sensitivity analysis for various threshold values is performed, investigating the impact of adopting the cumulative distribution functioned (CDF) controller to various performance metrics. The results illustrate an increase of up to 13% in the ride comfort of the passengers and increase of 6% in the road holding of the vehicle. Both are achieved by minimizing the switches of the damping ratio up to 80%. [ABSTRACT FROM AUTHOR]

In this work, we develop a rules-based µ -estimation algorithm which is independent of any tyre model and may be employed independently by brake control systems of the vehicle. The algorithm proposed herein aims at estimating the tyre/road friction coefficient during braking, in particular when one or more of the tyres approach the limit of adhesion. The proposed algorithm avoids the use of any particular tyre model and the need to identify several parameters of such models. It uses measurements of variables that are typically available in modern vehicles, namely, vehicle demanded and actual deceleration and wheel angular velocity, as well as the brake force distribution applied by the brake system. The algorithm is validated through implementation in high-fidelity vehicle dynamics simulation and by experimental data collected during experiments with a brake-by-wire electric vehicle. [ABSTRACT FROM AUTHOR]

Vehicles equipped with multiple electric machines allow variable distribution of propulsive and regenerative braking torques between axles or even individual wheels of the car. Left/right torque vectoring (i.e., a torque shift between wheels of the same axle) has been treated extensively in the literature; however, fewer studies focus on the torque shift between the front and rear axles, namely, front/rear torque vectoring, a drivetrain topology more suitable for mass production since it reduces complexity and cost. In this paper, we propose an online control strategy that can enhance vehicle agility and "fun-to-drive" for such a topology or, if necessary, mitigate oversteer during sublimit handling conditions. It includes a front/rear torque control allocation (CA) strategy that is formulated in terms of physical quantities that are directly connected to the vehicle dynamic behavior such as torques and forces, instead of nonphysical control signals. Hence, it is possible to easily incorporate the limitations of the electric machines and tires into the computation of the control action. Aside from the online implementation, this publication includes an offline study to assess the effectiveness of the proposed CA strategy, which illustrates the theoretical capability of affecting yaw moment that the front/rear torque vectoring strategy has for a given set of vehicle and road conditions and considering physical limitations of the tires and actuators. The development of the complete strategy is presented together with the results from hardware-in-the-loop (HiL) simulations, using a high fidelity vehicle model and covering various use cases. [ABSTRACT FROM AUTHOR]

Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep convolutional neural networks (CNN) in this paper. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, we propose a real-time nonlinear model predictive control (NMPC) strategy for stabilization of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model that neglects the wheel dynamics is coupled with a nonlinear tire model to design three MPC strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting quadratic program problem to compute the necessary longitudinal slips on the rear wheels, a second one that employs the real-time iteration scheme on the NMPC problem, and a third one that applies the primal dual interior point method on the NMPC problem instead until convergence. Then, a sliding mode slip controller is used to compute the necessary torques on the rear wheels based on the requested longitudinal slips. After analyzing the relative tradeoffs in performance and computational cost between the three MPC strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high-fidelity environment. [ABSTRACT FROM AUTHOR]

Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation. [ABSTRACT FROM PUBLISHER]

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’  distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’   distribution as well as another previous feed-forward solution. [ABSTRACT FROM PUBLISHER]

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet. [ABSTRACT FROM AUTHOR]

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment. [ABSTRACT FROM AUTHOR]

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle's motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this paper is to develop and evaluate a haptic steering support when driving near the vehicle's handling limits [Haptic Support near the Limits (HSNL)]. The support aims to promote the driver's perception of the vehicle's behavior and handling capacity (the vehicle's internal model) by providing haptic cues on the steering wheel. The HSNL has been evaluated in a test track where 17 test subjects drove around a narrow-twisting tarmac circuit, a vehicle (Opel Astra G/B) equipped with a steering system able to provide variable steering feedback torque. The drivers were instructed to achieve maximum velocity through corners while receiving haptic steering feedback cues related to the vehicle's cornering potentials. The test-track tests led to the conclusion that haptic support reduced drivers' mental and physical demand without affecting their driving performance. [ABSTRACT FROM PUBLISHER]

The paper investigates the optimality of the handbrake cornering, a strategy widespread among rally drivers. Nonlinear optimal control techniques are used to mimic real driver behavior. A proper yet simple cost function is devised to induce the virtual optimal driver to control the car at its physical limits while using the handbrake technique. The optimal solution is validated against experimental data by a professional rally driver performing the handbrake technique on a loose off-road surface. The effects of road surface, inertial properties, center of mass position, and friction coefficient are analyzed to highlight that the optimality of the maneuver does not depend on the particular vehicle data set used. It turns out that the handbrake maneuvering corresponds to the minimum time and minimum (lateral) space strategy on a tight hairpin corner. The results contribute to the understanding of one of the so-called aggressive driving techniques. [ABSTRACT FROM AUTHOR]

Attempts to measure and model driver steering behavior have been so far mainly performed with driving simulators and time-invariant techniques. The goal of this paper was to quantify the driver's arms' time-variant admittance in real driving and to provide a range of parametrically fitted values on the estimated frequency response functions. The human arms' neuromuscular (NMS) admittance was estimated by applying torque disturbances on the steering wheel during real car test-track driving. To capture the time-variant behavior, the admittance was estimated using a 1.28-s sliding time window. The results showed that drivers adapt their admittance while cornering, exposing a variant behavior during different corners and driving speeds. The frequency response function (FRF) of the admittance while cornering has the properties of a second-order system. During cornering, drivers have increased stiffness values, whilst in straight driving, the FRFs resemble a second-order system (-40~dB/decade gain drop; double pole at low frequencies) for low frequencies, with a zero for frequencies above 6 Hz (on average). The FRFs during cornering were parametrically fitted to a second-order inertia–spring–damper model. The fitted parameter values can be used for NMS driver models and motivate the stability analysis of the combined closed-loop driver steering system. [ABSTRACT FROM PUBLISHER]

This paper investigates the minimum time/limit handling car manoeuvring through nonlinear optimal control techniques. The resulting ‘optimal driver’ controls the car at its physical limits. The focus is on cornering: different road surfaces (dry and wet paved road, dirt and gravel off-road) and transmission layouts (rear-wheel-drive, front-wheel-drive and all-wheel-drive) are considered. Low-drift paved circuit-like manoeuvres and aggressive/high-drift even counter-steering rally like manoeuvres are found depending on terrain/layout combinations. The results shed a light on the optimality of limit handling techniques. [ABSTRACT FROM AUTHOR]

This paper supplies a roadmap on how a researcher can effectively perform real vehicular experiments oriented to high-speed driving research. It provides detailed guidelines for constructing versatile low-cost instrumentation suitable to be fitted on race cars. The custom-built equipment, consisting of wheel-speed sensors, steering angle–torque sensors, electronic boards, etc., is thoroughly described. Furthermore, this paper depicts the required processing from raw measurements to user-friendly data suitable for driver behavior studies. As an illustration, a case study on driving behavior analysis is presented, during the execution of high-speed circular maneuvers. The recorded data showed markedly different driving behaviors between expert and novice drivers. The mechanical designs and the open-source-based software are freely available online. [ABSTRACT FROM PUBLISHER]

The LuGre dynamic point contact friction model for the two-dimensional translation of a body on a surface has been used to derive a model for the friction forces and moments at the contact patch of a tire. The resulting tire friction model is distributed, i.e., is described by a set of partial differential equations. Several approximations have been used in the literature to approximate this distributed model using a set of ordinary differential equations, making the model more appropriate for control design and on-line estimation. In this paper, the method of moments is used to derive a set of ordinary differential equations to describe the exact average dynamics of the distributed model. Three cases of normal load distribution are considered and compared with each other uniform, trapezoidal and quartic load distribution. Simulations are also presented to compare with existing approximate lumped models. [ABSTRACT FROM AUTHOR]

In this paper we derive a new dynamic friction force model for the longitudinal road/tire interaction for wheeled ground vehicles. The model is based on a dynamic friction model developed previously for contact-point friction problems, called the LuGre model [1]. By assuming a contact patch between the tire and the ground we develop a partial differential equation for the distribution of the friction force along the patch. An ordinary differential equation (the lumped model) for the friction force is developed, based on the patch boundary conditions and the normal force distribution along the contact patch. This lumped model is derived to approximate closely the distributed friction model. Contrary to common static friction/slip maps, it is shown that this new dynamic friction model is able to capture accurately the transient behaviour of the friction force observed during transitions between braking and acceleration. A velocity-dependent, steady-state expression of the friction force versus the slip coefficient is also developed that allows easy tuning of the model parameters by comparison with steady-state experimental data. Experimental results validate the accuracy of the new tire friction model in prediction the friction force during transient vehicle motion. It is expected that this new model will be very helpful for tire friction modeling as well as for anti-lock braking (ABS) and fraction control design. [ABSTRACT FROM AUTHOR]