819 research outputs found
Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and B\'{e}zier Curves
Safety-guaranteed motion planning is critical for self-driving cars to
generate collision-free trajectories. A layered motion planning approach with
decoupled path and speed planning is widely used for this purpose. This
approach is prone to be suboptimal in the presence of dynamic obstacles.
Spatial-temporal approaches deal with path planning and speed planning
simultaneously; however, the existing methods only support simple-shaped
corridors like cuboids, which restrict the search space for optimization in
complex scenarios. We propose to use trapezoidal prism-shaped corridors for
optimization, which significantly enlarges the solution space compared to the
existing cuboidal corridors-based method. Finally, a piecewise B\'{e}zier curve
optimization is conducted in our proposed corridors. This formulation
theoretically guarantees the safety of the continuous-time trajectory. We
validate the efficiency and effectiveness of the proposed approach in numerical
and CommonRoad simulations.Comment: Under Review at ACC 202
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
Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms
Towards a Safe Real-Time Motion Planning Framework for Autonomous Driving Systems: An MPPI Approach
peer reviewedPlanning safe trajectories in Autonomous Driving Systems (ADS) is a complex
problem to solve in real-time. The main challenge to solve this problem arises
from the various conditions and constraints imposed by road geometry, semantics
and traffic rules, as well as the presence of dynamic agents. Recently, Model
Predictive Path Integral (MPPI) has shown to be an effective framework for
optimal motion planning and control in robot navigation in unstructured and
highly uncertain environments. In this paper, we formulate the motion planning
problem in ADS as a nonlinear stochastic dynamic optimization problem that can
be solved using an MPPI strategy. The main technical contribution of this work
is a method to handle obstacles within the MPPI formulation safely. In this
method, obstacles are approximated by circles that can be easily integrated
into the MPPI cost formulation while considering safety margins. The proposed
MPPI framework has been efficiently implemented in our autonomous vehicle and
experimentally validated using three different primitive scenarios.
Experimental results show that generated trajectories are safe, feasible and
perfectly achieve the planning objective. The video results as well as the
open-source implementation are available at:
https://gitlab.uni.lu/360lab-public/mpp
Intent prediction of vulnerable road users for trusted autonomous vehicles
This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions
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