Browsing by Author "Velenis, Efstathios"
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Item Open Access Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision(IEEE, 2018-05-01) Xing, Yang; Lv, Chen; Chen, Long; Wang, Huaji; Wang, Hong; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-YueLane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.Item Open Access Chapter 192: Integration of torque blending and slip control using nonlinear model predictive control(Unknown, 2016-09-30) Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, StefanoAntilock Braking System (ABS) is an important active safety feature in preventing accidents during emergency braking. Electrified vehicles which include both hydraulic and regenerative braking systems provide the opportunity to implement brake torque blending during slip control operation. This study evaluates the design and implementation of a new torque allocation algorithm using a Nonlinear Model Predictive Control (NMPC) strategy that can run in real-time, with results showing that wheel-locking can be prevented while also permitting for energy recuperation.Item Open Access Chapter 197: Evaluation of optimal yaw rate reference for closed-loop electric vehicle torque vectoring(Unknown, 2016-09-30) Smith, Edward N.; Tavernini, Davide; Velenis, Efstathios; Cao, DongpuThis work evaluates the intrinsic contribution of the yaw rate reference to the overall handling performance of an electric vehicle with torque vectoring control - in terms of minimum-time manoeuvring. A range of yaw rate references are compared through optimal control simulations incorporating closed-loop controller dynamics. Results show yaw rate reference has a significant effect on manoeuvre time.Item Open Access Control for motion sickness minimisation in autonomous vehicles.(Cranfield University, 2021-06) Htike, Zaw Lin; Longo, Stefano; Velenis, EfstathiosAutomated vehicles are expected to push towards the evolution of transportation systems and exploit the use of vehicular technologies. This thesis investigates the fundamentals of motion planning for minimising motion sickness in transportation systems of higher automation levels. The optimum velocity pro le 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 minimise the motion sickness and the journey time. Motion sick- ness is minimised by taking the optimum trajectory and velocity profile for any given road path generated by the motion planner. The trajectory tracking controllers based on PID control method were able to track the reference trajectory with good performances. The trade-off between motion sickness and journey time was solved using the application of multi-objective optimisation by altering the weighting factors to find a compromise solution. The Pareto front representing the correlation between the two components is obtained and this front also allows user to select their preference driving style. From the three case studies, driving styles have a bigger impact on reducing motion sickness and journey time rather than vehicle speed and the road width. However, the effect of road width is negligible when travelling on longer road for the reduction of motion sickness and journey time. This finding is crucial considering the need for automated vehicles to drive on a fixed road path in respect to road safety and also to allow the employment of connected and automated vehicles in the future. Finally, an approach combining two optimisation algorithms, the optimal control problem and the k - є method, is applied successfully to seek the best trajectory profile that ensures the optimum compromise between motion comfort and driving behaviour, energy efficiency, vehicle stability, occupant's confidence to ride and journey time.Item Open Access Driver activity recognition for intelligent vehicles: a deep learning approach(IEEE, 2019-04-01) Xing, Yang; Lv, Chen; Wang, Huaji; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-YueDriver 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 study. 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 (GMM) 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 analysed and discussed.Item Open Access Driver distraction detection using experimental methods and machine learning algorithms.(Cranfield University, 2020-02) Zhang, Zhaozhong; Velenis, Efstathios; Fotouhi, Abbas; Auger, Daniel J.Driver distraction causes numerous road accidents, which is approximately equal to 25% of the total crashes according to the reports by the National Highway Traffic Safety Administration. Warnings can be helpful to mitigate the risks caused by driver distraction. Previous studies on driver distraction detection have not sufficiently found relevant input features to filter insignificant information, thus limiting the improvement of efficiency. Moreover, the disadvantages of driving simulators and public roads pose a challenge in collecting suitable data for feature identification and comparisons of performance among driver distraction detection algorithms. While the previous research focuses on improving prediction accuracy, shortening the prediction time is critical in giving timely warnings to drivers. This thesis aims at detecting driver distraction, which could provide faster and accurate warnings to drivers. The developed method is implemented by cutting the redundancy and irrelevant information fed to the algorithms and instead selecting suitable algorithms that achieve the balance between the prediction accuracy and prediction time. Moreover, a closed testing field supplies an environment for collecting more accurate information to identify the relevant features and to determine suitable algorithms. In this study, open-source data and experimental data are used. The results show that a balance between the prediction accuracy and the prediction time is achieved by feeding the relevant features and using suitable machine learning algorithms (e.g. Decision Tree). Compared with existing state-of-the-art methods, the prediction accuracy of the method proposed in this study has reached approximately the same level. More importantly, the efficiency has improved, including reduced prediction time and fewer input features. Consequently, less computer storage is used.Item Open Access Driver distraction detection using machine learning algorithms – an experimental approach(Inderscience, 2021-05-08) Zhang, Zhaozhong; Velenis, Efstathios; Fotouhi, Abbas; Auger, Daniel J.; Cao, DongpuDriver distraction is the leading cause of accidents that contributes to 25% of all road crashes. In order to reduce the risks posed by distraction, warning must be given to the driver once distraction is detected. According to the literature, no rankings of relevant features have been presented. In this study, the most relevant features in detecting driver distraction are identified in a closed testing environment. The relevant features are found to be the mean values of speed and lane deviation, maximum values of eye gaze in direction, and head movement in direction. After the relevant features have been identified, pre-processed data with relevant features are fed into decision tree classifiers to discriminate the data into normal and distracted driving. The results show that detection accuracy of 78.4% using decision tree can be achieved. By eliminating unhelpful features, the time required to process data is reduced by around 40% to make the proposed technique suitable for real-time application.Item Open Access Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges(IEEE, 2019-03-06) Xing, Yang; Lv, Chen; Wang, Huaji; Wang, Hong; Ai, Yunfeng; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-YueIntelligent 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 study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus 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 consists 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 (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.Item Open Access Driver lane change intention inference using machine learning methods.(2018-04) Xing, Yang; Cao, Dongpu; Velenis, EfstathiosLane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.Item Open Access Effect of handling characteristics on minimum time cornering with torque vectoring(Taylor and Francis, 2017-09-12) Smith, Edward N.; Velenis, Efstathios; Tavernini, Davide; Cao, DongpuIn this paper, the effect of both passive and actively-modified vehicle handling characteristics on minimum time manoeuvring for vehicles with 4-wheel torque vectoring (TV) capability is studied. First, a baseline optimal TV strategy is sought, independent of any causal control law. An optimal control problem (OCP) is initially formulated considering 4 independent wheel torque inputs, together with the steering angle rate, as the control variables. Using this formulation, the performance benefit using TV against an electric drive train with a fixed torque distribution, is demonstrated. The sensitivity of TV-controlled manoeuvre time to the passive understeer gradient of the vehicle is then studied. A second formulation of the OCP is introduced where a closed-loop TV controller is incorporated into the system dynamics of the OCP. This formulation allows the effect of actively modifying a vehicle's handling characteristic via TV on its minimum time cornering performance of the vehicle to be assessed. In particular, the effect of the target understeer gradient as the key tuning parameter of the literature-standard steady-state linear single-track model yaw rate reference is analysed.Item Open Access Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective(IEEE, 2017-06-16) Marina Martinez, Clara; Hu, Xiaosong; Cao, Dongpu; Velenis, Efstathios; Gao, Bo; Wellers, MatthiasPlug-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.Item Open Access An ensemble deep learning approach for driver lane change intention inference(Elsevier, 2020-04-23) Xing, Yang; Lv, Chen; Wang, Huaji; Cao, Dongpu; Velenis, EfstathiosWith the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.Item Open Access Feedback brake distribution control for minimum pitch(Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2017-03-03) Tavernini, Davide; Velenis, Efstathios; Longo, StefanoThe 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.Item Open Access A framework for self-enforced optimal interaction between connected vehicles(IEEE, 2020-05-06) Stryszowski, Marcin; Longo, Stefano; D'Alessandro, Dario; Velenis, Efstathios; Forostovsky, Gregory; Manfredi, SabatoThis 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.Item Open Access Fundamentals of motion planning for mitigating motion sickness in automated vehicles(IEEE, 2021-12-28) Htike, Zaw; Papaioannou, Georgios; Siampis, Efstathios; Velenis, Efstathios; Longo, StefanoThis 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 evaluatemotion sickness. The tradeo 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 eect of driving style, vehicle speed, and road width, which can be considered among the main factors aecting 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 eect 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.Item Open Access Human-like motorway lane change trajectory planning for autonomous vehicles.(2019-10) Chang, Chun-Wei; Velenis, Efstathios; Fotouhi, Abbas; Longo, StefanoThe human lifestyle can be foreseen to have a tremendous change once the automation of transportation has been fully realised. The majority of current researches merely focus on improving the efficiency performance of autonomous vehicles(e.g. the energy management system, the handling, etc.)instead of putting the human acceptance and preference into consideration, leaving the knowledge gap of achieving the personalised automation. The primary objective of this research is to develop a novel human-like trajectory planning algorithm that is able to mimic the performance of human drivers and generate a feasible trajectory for an autonomous vehicle to complete a motorway lane change, which is the most representative and commonest manoeuvre on the motorway. This thesis can be divided into four main sections. Starting with the part of literature review, which summarises the existing techniques and the associated knowledges that can be taken the advantage of; including the trajectory planning, the driving styles, the lane change manoeuvre and the Model Predictive Control (MPC). An appropriate-designed experiment is then introduced and implemented, with the purpose of constructing a precise and reliable human driving database. This database contains 551 lane changes on the motorway from 12 different male drivers. Through applying data statistics methods, the human characteristics can be mined from the experimental data, showing that the vehicle velocity 𝒗, the hand steering wheel angle 𝜹 𝒉𝒂𝒏𝒅𝒓𝒆𝒂𝒍, the longitudinal acceleration a𝒙, the rate of hand steering 𝜸 𝒉𝒂𝒏𝒅𝒔𝒕𝒆𝒆𝒓 and the rate of longitudinal accelerating 𝜸 𝒍𝒐𝒏𝒈𝑨𝒄𝒄 are the essential features for the motorway lane change manoeuvre. An off-line constraint table for the three nominated driving styles can be therefore constructed based on these features. Finally, the obtained human information is then fused with the traditional MPC planning technique so as to achieve the proposed human-like trajectory planning algorithm. The main contribution of this study is proposing a novel approach of combining the real human driving data and the traditional planning technique(i.e. MPC) to achieve human-like lane change trajectory planning for autonomous vehicles. An integrated human driving database which contains both the video footages and the vehicle-dynamic-based signals from 12 different participants is built. Moreover, the draft marginal values of the essential parameters for the driving styles while performing a right lane change on the motorway are also presented. Both the collected driving database and the driving styles’ constraint table can be seen as distinctive achievements, providing resourceful materials for future researches.Item Open Access Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition(IEEE, 2018-12-25) Xing, Yang; Lv, Chen; Zhang, Zhaozhong; Wang, Huaji; Na, Xiaoxiang; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-YueDriver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.Item Open Access Integrated Path-tracking and Control Allocation Controller for Autonomous Electric Vehicle under Limit Handling Condition(IEEE, 2021-01-08) Li, Boyuan; Ahmadi, Javad; Lin, Chenhui; Siampis, Efstathios; Longo, Stefano; Velenis, EfstathiosIn current literature, a number of studies have separately considered path-tracking (PT) control and control allocation (CA) method, but few of studies have integrated them together. This study proposes an integrated PT and CA method for autonomous electric vehicle with independent steering and driving actuators in the limit handling scenario. The high-level feedback PT controller can determine the desired total tire forces and yaw moment, and is designed to guarantee yaw angle error and lateral deviation converge to zero simultaneously. The low-level CA method is formulated as a compact quadratic programming (QP) optimization formulation to optimally allocate individual control actuator. This CA method is designed for a prototype experiment electric vehicle with particularly steering and driving actuator arrangement. The proposed integrated PT controller is validate through numerical simulation based on a high-fidelity CarMaker model on highspeed limit handling scenario.Item Open Access An integrated path-tracking and control allocation method for autonomous racing electric vehicles(Taylor & Francis, 2023-08-08) Li, Boyuan; Lin, Chenhui; Ahmadi, Javad; Siampis, Efstathios; Longo, Stefano; Velenis, EfstathiosIn 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.Item Open Access Integration of anti-lock braking system and regenerative braking for hybrid/electric vehicles(2017-11) Basrah, Mohd Sofian; Velenis, Efstathios; Cao, DongpuVehicle electrification aims at improving energy efficiency and reducing pollutant emissions which creates an opportunity to use the electric machines (EM) as Regenerative Braking System (RBS) to support the friction brake system. Anti-lock Braking System (ABS) is part of the active safety systems that help drivers to stop safely during panic braking while ensuring the vehicle’s stability and steerability. Nevertheless, the RBS is deactivated at a safe (low) deceleration threshold in favour of ABS. This safety margin results in significantly less energy recuperation than what would be possible if both RBS and ABS were able to operate simultaneously. Vehicle energy efficiency can be improved by integrating RBS and friction brakes to enable more frequent energy recuperation activations, especially during high deceleration demands. The main aim of this doctoral research is to design and implement new wheel slip control with torque blending strategies for various vehicle topologies using four, two and one EM. The integration between the two braking actuators will improve the braking performance and energy efficiency of the vehicle. It also enables ABS by pure EM in certain situations where the regenerative brake torque is sufficient. A novelmethod for integrating the wheel slip control and torque blending is developed using Nonlinear Model Predictive Control (NMPC). The method is well known for the optimal performance and enforcement of critical control and state constraints. A linear MPC strategy is also developed for comparison purpose. A pragmatic brake torque blending algorithm using Daisy-Chain with sliding mode slip control is also developed based on a pre-defined energy recuperation priority. Simulation using high fidelity model using co-simulation in Matlab/Simulink and CarMaker is used to validate the developed strategies. Different test patterns are used to evaluate the controllers’ performance which includes longitudinal and lateral motions of the vehicle. Comparison analysis is done for the proposed strategies for each case. The capability for real-time implementation of the MPC controllers is assessed in simulation testing using dSPACE hardware.