Local motion planning method using closed-loop forward simulation for autonomous vehicle

2016 ◽  
Author(s):  
Byungjae Park ◽  
Kiin Na ◽  
Jaemin Byun ◽  
Woo Young Han
Keyword(s):  
Motion Planning ◽  
Closed Loop ◽  
Autonomous Vehicle ◽  
Planning Method ◽  
Forward Simulation ◽  
Local Motion

Construction of Motion Planning Method for Autonomous Vehicle in Cyclist Overtaking Scenario

2018 ◽  
Vol 2018 (0) ◽  
pp. 1P2-A11
Author(s):  
Yuki WATANABE ◽  
Yuichi SAITO ◽  
Masahiko TANIMOTO ◽  
Haruo NAKATA ◽  
Yosuke ISHIWATARI ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicle ◽  
Planning Method

scholarly journals Vision-only fully automated driving in dynamic mixed-traffic scenarios

2015 ◽  
Vol 57 (4) ◽  
Author(s):  
Ulrich Schwesinger ◽  
Pietro Versari ◽  
Alberto Broggi ◽  
Roland Siegwart
Keyword(s):  
Motion Planning ◽  
Real World ◽  
Autonomous Vehicle ◽  
Wide Angle ◽  
Mixed Traffic ◽  
Local Motion ◽  
Automated Driving ◽  
Fully Automated Driving

AbstractIn this work an overview of the local motion planning and dynamic perception framework within the V-Charge project is presented. This framework enables the V-Charge car to autonomously navigate in dynamic mixed-traffic scenarios. Other traffic participants are detected, classified and tracked from a combination of stereo and wide-angle monocular cameras. Predictions of their future movements are generated utilizing infrastructure information. Safe motion plans are acquired with a system-compliant sampling-based local motion planner. We show the navigation performance of this vision-only autonomous vehicle in both simulation and real-world experiments.

scholarly journals Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Doopalam Tuvshinjargal ◽  
Byambaa Dorj ◽  
Deok Jin Lee
Keyword(s):  
Motion Planning ◽  
Sensor Fusion ◽  
Field Experiments ◽  
Autonomous Robots ◽  
Autonomous Vehicle ◽  
Dynamic Environments ◽  
Obstacle Detection ◽  
Planning Method ◽  
Moving Obstacles ◽  
Virtual Plane

A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

Lane-Change Planning with Dynamic Programming and Closed-Loop Forward Simulation for Autonomous Vehicle

2021 ◽  
Author(s):  
Yunpeng Li ◽  
Zhenwen Deng ◽  
Dequan Zeng ◽  
Yiming Hu ◽  
Peizhi Zhang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Closed Loop ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Forward Simulation

Local Motion Planning and Tracking of Autonomous Driving Vehicles*

2020 ◽  
Author(s):  
Wangwang Zhu ◽  
Xi Zhang ◽  
Baixuan Zhao ◽  
Shiwei Peng ◽  
Pengfei Guo ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Driving ◽  
Local Motion

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

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