Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one’s driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios

Motion Planning of Autonomous Vehicles on a Dual Carriageway without Speed Lanes

The problem of motion planning of an autonomous vehicle amidst other vehicles on a straight road is considered. Traffic in a number of countries is unorganized, where the vehicles do not move within predefined speed lanes. In this paper, we formulate a mechanism wherein an autonomous vehicle may travel on the “wrong” side in order to overtake a vehicle. Challenges include assessing a possible overtaking opportunity, cooperating with other vehicles, partial driving on the “wrong” side of the road and safely going to and returning from the “wrong” side. The experimental results presented show vehicles cooperating to accomplish overtaking manoeuvres

Robot Trajectories Comparison: A Statistical Approach

The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners

Future cities and autonomous vehicles: analysis of the barriers to full adoption

The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development

The relationship between aggressive driving and driver performance : a systematic review with meta-analysis

Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver’s performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver’s control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system

Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.