scholarly journals Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

2021 ◽  
Vol 11 (9) ◽  
pp. 3909
Author(s):  
Changhyeon Park ◽  
Seok-Cheol Kee
Keyword(s):  
Path Planning ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Campus Environment ◽  
Vehicle Localization ◽  
Time Operation ◽  
National University ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

scholarly journals Path Planning for Highly Automated Driving on Embedded GPUs

2018 ◽  
Vol 8 (4) ◽  
pp. 35 ◽  
Author(s):  
Jörg Fickenscher ◽  
Sandra Schmidt ◽  
Frank Hannig ◽  
Mohamed Bouzouraa ◽  
Jürgen Teich
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Computing Power ◽  
Planning Algorithm ◽  
Time Requirements ◽  
Evaluation Platform ◽  
Path Planning Algorithm ◽  
Highly Automated Driving

The sector of autonomous driving gains more and more importance for the car makers. A key enabler of such systems is the planning of the path the vehicle should take, but it can be very computationally burdensome finding a good one. Here, new architectures in ECU are required, such as GPU, because standard processors struggle to provide enough computing power. In this work, we present a novel parallelization of a path planning algorithm. We show how many paths can be reasonably planned under real-time requirements and how they can be rated. As an evaluation platform, an Nvidia Jetson board equipped with a Tegra K1 SoC was used, whose GPU is also employed in the zFAS ECU of the AUDI AG.

scholarly journals A Locking Sweeping Method Based Path Planning for Unmanned Surface Vehicles in Dynamic Maritime Environments

2020 ◽  
Vol 8 (11) ◽  
pp. 887
Author(s):  
Jiayuan Zhuang ◽  
Jing Luo ◽  
Yuanchang Liu
Keyword(s):  
Path Planning ◽  
Unmanned Surface Vehicles ◽  
Intelligent Decision Making ◽  
Moving Obstacles ◽  
Time Operation ◽  
Real Time Operation ◽  
Planning Algorithm ◽  
Sweeping Method ◽  
Planning Algorithms ◽  
Path Planning Algorithm

Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships’ velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories.

scholarly journals Path Planning for Smart Car Based on Dijkstra Algorithm and Dynamic Window Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Li-sang Liu ◽  
Jia-feng Lin ◽  
Jin-xin Yao ◽  
Dong-wei He ◽  
Ji-shi Zheng ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Driving ◽  
Initial Position ◽  
Depth Information ◽  
Dijkstra Algorithm ◽  
Planning Algorithm ◽  
Local Path ◽  
Window Approach ◽  
Path Planning Algorithm

Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self-constructed smart obstacle avoidance car, which used a LeTMC-520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.

scholarly journals Reinforcement Learning Path Planning Method with Error Estimation

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Feihu Zhang ◽  
Can Wang ◽  
Chensheng Cheng ◽  
Dianyu Yang ◽  
Guang Pan
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Error Estimation ◽  
Autonomous Driving ◽  
Planning Method ◽  
Robot Kinematics ◽  
Learning Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Accumulated Error

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

scholarly journals A Novel Path Planning Algorithm for Truck Platooning Using V2V Communication

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7022
Author(s):  
Yongki Lee ◽  
Taewon Ahn ◽  
Chanhwa Lee ◽  
Sangjun Kim ◽  
Kihong Park
Keyword(s):  
Path Planning ◽  
Autonomous Driving ◽  
Global Coordinate System ◽  
String Stability ◽  
Vehicle Communication ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Planning Algorithm ◽  
Target Path ◽  
Path Planning Algorithm

In truck platooning, the leading vehicle is driven manually, and the following vehicles run by autonomous driving, with the short inter-vehicle distance between trucks. To successfully perform platooning in various situations, each truck must maintain dynamic stability, and furthermore, the whole system must maintain string stability. Due to the short front-view range, however, the following vehicles’ path planning capabilities become significantly impaired. In addition, in platooning with articulated cargo trucks, the off-tracking phenomenon occurring on a curved road makes it hard for the following vehicle to track the trajectory of the preceding truck. In addition, without knowledge of the global coordinate system, it is difficult to correlate the local coordinate systems that each truck relies on for sensing environment and dynamic signals. In this paper, in order to solve these problems, a path planning algorithm for platooning of articulated cargo trucks has been developed. Using the Kalman filter, V2V (Vehicle-to-Vehicle) communication, and a novel update-and-conversion method, each following vehicle can accurately compute the trajectory of the leading vehicle’s front part for using it as a target path. The path planning algorithm of this paper was validated by simulations on severe driving scenarios and by tests on an actual road. The results demonstrated that the algorithm could provide lateral string stability and robustness for truck platooning.

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