Throughout the last century, the automobile industry achieved remarkable milestones in manufacturing reliable, safe, and affordable vehicles. Because of significant recent advances in computation and communication technologies, autonomous cars are becoming a reality. Already autonomous car prototype models have covered millions of miles in test driving. Leading technical companies and car manufacturers have invested a staggering amount of resources in autonomous car technology, as they prepare for autonomous cars’ full commercialization in the coming years. However, to achieve this goal, several technical and nontechnical issues remain: software complexity, real-time data analytics, and testing and verification are among the greater technical challenges; and consumer stimulation, insurance management, and ethical/moral concerns rank high among the nontechnical issues. Tackling these challenges requires thoughtful solutions that satisfy consumers, industry, and governmental requirements, regulations, and policies. Thus, here we present a comprehensive review of state-of-the-art results for autonomous car technology. We discuss current issues that hinder autonomous cars’ development and deployment on a large scale. We also highlight autonomous car applications that will benefit consumers and many other sectors. Finally, to enable cost-effective, safe, and efficient autonomous cars, we discuss several challenges that must be addressed (and provide helpful suggestions for adoption) by designers, implementers, policymakers, regulatory organizations, and car manufacturers.

Autonomous Cars: Research Results, Issues, and Future Challenges

Model Predictive Control-based Efficient Energy Recovery Control Strategy for Regenerative Braking System of Hybrid Electric Bus

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain–machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human–automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.

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A Topology of Shared Control Systems—Finding Common Ground in Diversity

Cooperative driving systems may increase the utilization of road infrastructure resources through coordinated control and platooning of individual vehicles with the potential of enhancing both traffic safety and efficiency. Vehicle cooperative driving is essentially a hybrid system that is a combination of discrete events, i.e., the transition of discrete cooperative maneuvering modes, such as vehicle merging and platoon splitting, as well as continuous vehicle dynamics. In this paper, a novel hybrid system consisting of the discrete cooperative maneuver switch and the continuous vehicle motion control is introduced into a multi-vehicle cooperative control system with a distributed control structure, leading each automated vehicle to conduct path planning and motion control separately. The primary novelty of this paper lies in that it presents a control algorithm combining artificial potential field (APF) approach with model predictive control (MPC), and using the optimizer of the MPC controller to replace the gradient-descending method in the traditional APF approach. Such a method can accomplish both path planning and motion control synchronously. Second, based on hybrid automata, a cooperative maneuver switching model consisting of a system state set and a discrete maneuver transition rule is established for two discrete maneuvers in the cooperative driving system, i.e., single-vehicle cruising and multiple-vehicle platooning. Simulations in several typical traffic scenarios demonstrate the effectiveness of the proposed method.

Path Planning and Cooperative Control for Automated Vehicle Platoon Using Hybrid Automata

Development of vehicle active steering collision avoidance systems calls for mathematical models capable of predicting a human driver's response so as to reduce the cost involved in field tests while accelerating product development. This paper provides a discussion on the paradigms that may be used for modeling a driver's steering interaction with vehicle collision avoidance control in path-following scenarios. Four paradigms, namely decentralized, noncooperative Nash, noncooperative Stackelberg, and cooperative Pareto are established. The decentralized paradigm, which is developed on the basis of optimal control theory, represents a driver's interaction with the collision avoidance controllers that disregard driver steering control. The noncooperative Nash and Stackelberg paradigms are used for predicting a driver's steering behavior in response to the collision avoidance control that actively compensates for driver steering action. These two are devised based on the principles of equilibria in noncooperative game theory. The cooperative Pareto paradigm is derived from cooperative game theory to model a driver's interaction with the collision avoidance systems that take into account the driver's target path. The driver and the collision avoidance controllers’ optimization problems and their resulting steering strategies arise in each paradigm are delineated. Two mathematical approaches applicable to these optimization problems namely the distributed model predictive control and the linear quadratic dynamic optimization approaches are described in detail. A case study illustrating a conflict in steering control between driver and vehicle collision avoidance system is performed via simulation. It was found that the variation of driver path-error cost function weights results in a variety of steering behaviors, which are distinct between paradigms.

Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller

This paper presents a driving simulator experiment, which evaluates a road-departure prevention (RDP) system in an emergency situation. Two levels of automation are evaluated: 1) haptic feedback (HF) where the RDP provides advisory steering torque such that the human and the machine carry out the maneuver cooperatively, and 2) drive by wire (DBW) where the RDP automatically corrects the front-wheels angle, overriding the steering-wheel input provided by the human. Thirty participants are instructed to avoid a pylon-confined area while keeping the vehicle on the road. The results show that HF has a significant impact on the measured steering wheel torque, but no significant effect on steering-wheel angle or vehicle path. DBW prevents road departure and tends to reduce self-reported workload, but leads to inadvertent human-initiated steering resulting in pylon collisions. It is concluded that a low level of automation, in the form of HF, does not prevent road departures in an emergency situation. A high level of automation, on the other hand, is effective in preventing road departures. However, more research may have to be done on the human response while driving with systems that alter the relationship between steering-wheel angle and front-wheels angle.

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Road-Departure Prevention in an Emergency Obstacle Avoidance Situation

A driver-assistance system (DAS) that combines an automatic road departure avoidance (RDA) system with the driver's steering input is presented. The system is based on a closed-loop driver decision estimator (DDE), which establishes the risk of road departure. If a road departure is likely to occur, the RDA system intervenes by correcting the steering angle. Robustness is guaranteed by an Hinf controller designed to take into consideration the main uncertainties affecting the dynamics. Driver-in-the-loop tests evaluate the performance of the system in terms of robustness, risk of road departure, and driving experience. Experiments show that the system is effective in reducing the risk of road departure without impeding maneuverability.

A Robust Steering Assistance System for Road Departure Avoidance

A driving simulator experiment is presented investigating different road departure prevention (RDP) setups. To induce the risk of road departure, thirty test drivers were asked to avoid a pylon-confined area (obstacle) while keeping the vehicle within the road limits. The RDP system intervened by applying a haptic-feedback (i.e., haptic shared control) and/or correcting the steering angle (i.e., drive-by-wire (DBW) input-mixing shared control) in the event that a vehicle road departure was likely to occur. The system that determines the correcting steering input is a RDP controller based on the driver's inputs. The results showed that DBW effectively helped drivers to stay within road limits and reduced workload. The haptic shared control had a significant influence on the measured steering torque, but limited effect on the steering wheel angle and the vehicle path. The DBW system resulted in drivers making counter-corrections demoting their performance. In conclusion, shared control for RDP is effective, although more research needs to be conducted regarding the human response in situations where the relationship between the steering wheel angle and the front wheels' steering angle is altered while driving.

Shared control for road departure prevention

In this paper, in addition to the main functionality of vehicle following, cooperative adaptive cruise control (CACC) is enabled with additional features of gap closing and collision avoidance. Due to its nonlinear nature, a control objective such as collision avoidance, cannot be addressed using a linear controller such as a PD controller. However, the artificial potential functions can be adopted to design controllers which accommodate multiple (nonlinear) control objectives in a single design. By defining an appropriate control law, the system state is always driven to the minima of the designed potential function which guarantees the required performance of the system.

Cooperative adaptive cruise control: An artificial potential field approach

In this paper the systematic development of an integrated braking controller for a vehicle driven by an electric motor on the front axle is presented. The objective is to engage the electric motor only during braking, up to the point at which the vehicle reaches its manoeuvrability and stability limit. The control challenges are to distribute the braking effort correctly between the hydraulic brakes at the four tyres and the electric motor, to handle the tyre saturation and motor constraints effectively and to adapt the control allocation based on the vehicle’s states. The controller is designed using the state-dependent Riccati equation control technique, the vehicle state estimation and the ‘magic formula’ tyre model. The state-dependent Riccati equation control technique is a suboptimal control design technique for non-linear systems. A novel method for constructing the state-dependent coefficient formulation of the system dynamics is proposed. Soft constraints in the state dynamics are described, while an augmented penalty approach is suggested for handling the system’s hard constraints. The performance of the controller was evaluated for different braking scenarios using simulations in a MATLAB/Simulink environment. An eight-degree-of-freedom non-linear vehicle model was utilized. The numerical results show that the controller suboptimizes the regenerative braking effort while considering the tyre force saturation, the motor torque limits, the vehicle yaw rate and the slip angle error. A comparison with a constrained linear quadratic regulator shows the advantages of the proposed controller.

Control allocation for regenerative braking of electric vehicles with an electric motor at the front axle using the state-dependent Riccati equation control technique

Mohsen Alirezaei

Papers

Road-Departure Prevention in an Emergency Obstacle Avoidance Situation

A Robust Steering Assistance System for Road Departure Avoidance

Shared control for road departure prevention

Cooperative adaptive cruise control: An artificial potential field approach

Control allocation for regenerative braking of electric vehicles with an electric motor at the front axle using the state-dependent Riccati equation control technique