215 research outputs found
Application of Sampling-Based Motion Planning Algorithms in Autonomous Vehicle Navigation
With the development of the autonomous driving technology, the autonomous vehicle has become one of the key issues for supporting our daily life and economical activities. One of the challenging research areas in autonomous vehicle is the development of an intelligent motion planner, which is able to guide the vehicle in dynamic changing environments. In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling-based algorithms. Furthermore, a novel segmentation method is proposed, which divides the sampling domain into valid and tabu segments. The resulted navigation architecture is able to guide the autonomous vehicle in complex situations such as takeover or crowded environments. The performance of the proposed method is tested through simulation in different scenarios and also by comparing the performances of RRT and RRT* algorithms. The proposed method provides near-optimal solutions with smaller trees and in lower running time
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
To achieve optimal robot behavior in dynamic scenarios we need to consider
complex dynamics in a predictive manner. In the vehicle dynamics community, it
is well know that to achieve time-optimal driving on low surface, the vehicle
should utilize drifting. Hence many authors have devised rules to split
circuits and employ drifting on some segments. These rules are suboptimal and
do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the
question "When to go into which mode and how to drive in it?" remains
unanswered. To choose the suitable mode (discrete decision), the algorithm
needs information about the feasibility of the continuous motion in that mode.
This makes it a class of Task and Motion Planning (TAMP) problems, which are
known to be hard to solve optimally in real-time. In the AI planning community,
search methods are commonly used. However, they cannot be directly applied to
TAMP problems due to the continuous component. Here, we present a search-based
method that effectively solves this problem and efficiently searches in a
highly dimensional state space with nonlinear and unstable dynamics. The space
of the possible trajectories is explored by sampling different combinations of
motion primitives guided by the search. Our approach allows to use multiple
locally approximated models to generate motion primitives (e.g., learned models
of drifting) and effectively simplify the problem without losing accuracy. The
algorithm performance is evaluated in simulated driving on a mixed-track with
segments of different curvatures (right and left). Our code is available at
https://git.io/JenvBComment: Accepted to the journal Engineering Applications of Artificial
Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin
note: text overlap with arXiv:1907.0782
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
Conception of control paradigms for teleoperated driving tasks in urban environments
Development of concepts and computationally efficient motion planning methods for teleoperated drivingEntwicklung von Konzepten und recheneffizienten Bewegungsplanungsmethoden für teleoperiertes Fahre
Validation of trajectory planning strategies for automated driving under cooperative, urban, and interurban scenarios.
149 p.En esta Tesis se estudia, diseña e implementa una arquitectura de control para vehículos automatizados de forma dual, que permite realizar pruebas en simulación y en vehículos reales con los mínimos cambios posibles. La arquitectura descansa sobre seis módulos: adquisición de información de sensores, percepción del entorno, comunicaciones e interacción con otros agentes, decisión de maniobras, control y actuación, además de la generación de mapas en el módulo de decisión, que utiliza puntos simples para la descripción de las estructuras de la ruta (rotondas, intersecciones, tramos rectos y cambios de carril)Tecnali
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