Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people’s way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.

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A Review of Motion Planning for Highway Autonomous Driving

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver–vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models.

Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving.

To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours. Within the decision-making framework, a motion prediction module is designed and optimized using model predictive control (MPC) to enhance the effectiveness and accuracy of the decision-making algorithm. Besides, the payoff function of decision making is defined with the consideration of vehicle safety, ride comfort and travel efficiency. Additionally, the constraints of the decision-making problem are constructed. Based on the established decision-making model, Stackelberg game and grand coalition game approaches are adopted to address the decision making of CAVs at an unsignalized roundabout. Three testing cases considering personalized driving behaviours are carried out to verify the performance of the developed decision-making algorithms. The testing results show that the proposed game theoretic decision-making framework is able to make safe and reasonable decisions for CAVs in the complex urban scenarios, validating its feasibility and effectiveness. Stackelberg game approach shows its advantage in guaranteeing personalized driving objectives of individuals, while the grand coalition game approach is advantageous regarding the efficiency improvement of the transportation system.

Decision Making of Connected Automated Vehicles at an Unsignalized Roundabout Considering Personalized Driving Behaviours

To reduce the number of collision fatalities at crossroads intersections, many countries have started replacing intersections with non-signalized roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviors according to the scenario. A non-signalized roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction-dependent scenarios safely, efficiently, and comfortably has been a challenge even for human drivers. Unlike traffic signal-controlled roundabouts, where the merging order is centrally controlled, driving a non-signalized roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an adaptive tactical behavior planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviors for navigating a non-signalized roundabout, combining naturalistic behavior planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behavior planning approach and the ATBP design is described in this paper.

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Autonomous Navigation in Interaction-Based Environments—A Case of Non-Signalized Roundabouts

As autonomous technologies in ground vehicle application begin to mature, there is a greater acceptance that they can eventually exhaust human involvement in the driving activity. There is however still a long way to go before such maturity is seen in autonomous ground vehicles. One of the critical limitations of the existing technology is the inability to navigate complex dynamic traffic scenarios such as non-signalised roundabouts safely, efficiently and while maintaining passenger drive comfort. The navigation at roundabouts has often been considered as either a problem of collision avoidance alone or the problem of efficient driving (reducing congestion). We argue that for any autonomous planning solution to be accepted for replacing the human driver, it has to consider all the three objectives of safety, efficiency and comfort. With human drivers driving these complex and dynamic scenarios for a long time, learning from the human driving has become a promising area of research. In this work, we learn human driver's longitudinal behaviours for driving at a non-signalised roundabout. This knowledge is then used to generate longitudinal behaviour candidate profiles that give the autonomous vehicle different behaviour choices in a dynamic environment. A decision-making algorithm is then employed to tactically select the optimal behaviour candidate based on the existing scenario dynamics. There are two important contributions in this paper, firstly the adaptive longitudinal behaviour candidate generation algorithm and secondly the tactical, risk aware, multi-objective decision-making algorithm. We describe their implementation and compare the autonomous vehicle performance against human driving.

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Adaptive behaviour selection for autonomous vehicle through naturalistic speed planning

The ability of autonomous vehicles to successfully replace human drivers depends on their capability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through complex scenarios such as intersections which demand interactive behaviour between vehicles. Many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state-of-the-art of path planning solutions. They have been found to be inefficient and sometimes unsafe when tackling interactively demanding scenarios. The generic path planning solutions consists of three planners, a “global path planner”, a “behaviour planner” and a “local path planner. In this paper we establish that the “behaviour planner” is the limitation of a successful path planning solution, after reviewing the individual planners and the associated solutions. In this paper a new adaptive tactical behaviour planner is proposed to overcome the limitations. This planner is motivated by how expert human drivers behave in interactive scenarios, and is made up of a three module architecture. The paper describes the individual modules, and also highlights how they play a part in the overall behaviour selection for the autonomous vehicle. The paper is concluded by a discussion on how this proposed planner generates safe and efficient behaviours in complex dynamic traffic scenarios by considering a case of a roundabout not controlled by traffic signals.

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Adaptive tactical behaviour planner for autonomous ground vehicle

Automation in ground vehicles has been gaining momentum in recent years highlighted by the significant number of public demonstrations in the last two decades This momentum has created an urgency within research organizations, vehicle manufacturers and academia to solve existing problems with autonomous vehicle technology to make it usable in the real world As autonomous ground vehicles operate in close proximity to one another, the margin of error for navigation is smaller than in other domains such as aerospace and marine application The real-world driving scenarios for the autonomous ground vehicle can sometimes be predictable and unpredictable at other times, demanding different behaviours from the autonomous vehicle for successful navigation To satisfy such as requirement, the autonomous vehicle should exhibit the capability to adapt to through deliberative planning in predictable environments and reactive planning in unpredictable environments In this paper, we describe a hybrid control software framework designed to incorporate behaviour planning algorithms that are capable of both deliberative and reactive planning The paper describes the development of this novel adaptive autonomous control software framework and validates it through both virtual testing and real world testing environments

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Graham Gest

Papers

Autonomous Navigation in Interaction-Based Environments—A Case of Non-Signalized Roundabouts

Adaptive behaviour selection for autonomous vehicle through naturalistic speed planning

Adaptive tactical behaviour planner for autonomous ground vehicle

Developing and testing of control software framework for autonomous ground vehicle