4 research outputs found
Adaptive tactical behaviour planner for autonomous ground vehicle
Success of autonomous vehicle to effectively
replace a human driver depends on its ability 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 scenarios such as
intersections that demand interactive behavior for successful
navigation. The many autonomous vehicle demonstrations over
the last few decades have highlighted the limitations in the
current state of the art in path planning solutions. They have
been found to result in inefficient and sometime unsafe
behaviours when tackling interactively demanding scenarios. In
this paper we review the current state of the art of path planning
solutions, the individual planners and the associated methods
for each planner. We then establish a gap in the path planning
solutions by reviewing the methods against the objectives for
successful path planning. A new adaptive tactical behaviour
planner framework is then proposed to fill this gap. The
behaviour planning framework is motivated by how expert
human drivers plan their behaviours in interactive scenarios.
Individual modules of the behaviour planner is then described
with the description how it fits in the overall framework. Finally
we discuss how this planner is expected to generate safe and
efficient behaviors in complex dynamic traffic scenarios by
considering a case of an un-signalised roundabout
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
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