Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification
Self-driving vehicles is a highly anticipated technology for increasing the safety and efficiency of automotive transportation systems by removing the risk of human errors. Volvo Car Corporation is determined to produce vehicles of full autonomy and highest safety within the near future. To achieve...
Main Author: | |
---|---|
Format: | Other/Unknown Material |
Language: | English |
Published: |
Lunds universitet/Institutionen för reglerteknik
2019
|
Subjects: | |
Online Access: | http://lup.lub.lu.se/student-papers/record/8986376 |
id |
ftulundlupsp:oai:lup-student-papers.lub.lu.se:8986376 |
---|---|
record_format |
openpolar |
spelling |
ftulundlupsp:oai:lup-student-papers.lub.lu.se:8986376 2023-07-30T04:06:10+02:00 Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification Johansson, Daniel 2019 application/pdf http://lup.lub.lu.se/student-papers/record/8986376 eng eng Lunds universitet/Institutionen för reglerteknik http://lup.lub.lu.se/student-papers/record/8986376 ISSN: 0280-5316 Technology and Engineering H3 2019 ftulundlupsp 2023-07-11T20:09:47Z Self-driving vehicles is a highly anticipated technology for increasing the safety and efficiency of automotive transportation systems by removing the risk of human errors. Volvo Car Corporation is determined to produce vehicles of full autonomy and highest safety within the near future. To achieve this goal, Volvo Cars is in parallel with the development of autonomous vehicles setting up a sophisticated pipeline for verifying and testing the autonomous driving functions. This thesis revolves around the last step of this pipeline, by implementing and further developing an algorithm for collision avoidance in a robotized framework for verification of autonomous driving where the functions are tested on real vehicles on a test track. The proposed algorithm used to achieve collision avoidance in the robotized test framework is the Bicycle Optimal Reciprocal Collision Avoidance (B-ORCA) algorithm. This algorithm uses a construct called Velocity Obstacle to predict imminent collisions between vehicles in a scenario and then calculates the optimal velocities to avoid the collisions in a collaborative manner. To evaluate the performance of the algorithm, a set of experiments were performed on the driving robots that will be used in the testing framework, both in simulations and on a real vehicle. The results from these experiments show that the current implementation of the B-ORCA algorithm guarantees accurate safe trajectories up to speeds of 50km=h. To support speeds above 50km=h, the simple Kinematic bicycle model currently used to calculate the trajectories has to be replaced with a more sophisticated motion model. This new model has to better model the lateral acceleration that, with too high values, was shown to be the main parameter that made the vehicle not follow the safe trajectories as desired. Other/Unknown Material Orca Lund University Publications Student Papers (LUP-SP) |
institution |
Open Polar |
collection |
Lund University Publications Student Papers (LUP-SP) |
op_collection_id |
ftulundlupsp |
language |
English |
topic |
Technology and Engineering |
spellingShingle |
Technology and Engineering Johansson, Daniel Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
topic_facet |
Technology and Engineering |
description |
Self-driving vehicles is a highly anticipated technology for increasing the safety and efficiency of automotive transportation systems by removing the risk of human errors. Volvo Car Corporation is determined to produce vehicles of full autonomy and highest safety within the near future. To achieve this goal, Volvo Cars is in parallel with the development of autonomous vehicles setting up a sophisticated pipeline for verifying and testing the autonomous driving functions. This thesis revolves around the last step of this pipeline, by implementing and further developing an algorithm for collision avoidance in a robotized framework for verification of autonomous driving where the functions are tested on real vehicles on a test track. The proposed algorithm used to achieve collision avoidance in the robotized test framework is the Bicycle Optimal Reciprocal Collision Avoidance (B-ORCA) algorithm. This algorithm uses a construct called Velocity Obstacle to predict imminent collisions between vehicles in a scenario and then calculates the optimal velocities to avoid the collisions in a collaborative manner. To evaluate the performance of the algorithm, a set of experiments were performed on the driving robots that will be used in the testing framework, both in simulations and on a real vehicle. The results from these experiments show that the current implementation of the B-ORCA algorithm guarantees accurate safe trajectories up to speeds of 50km=h. To support speeds above 50km=h, the simple Kinematic bicycle model currently used to calculate the trajectories has to be replaced with a more sophisticated motion model. This new model has to better model the lateral acceleration that, with too high values, was shown to be the main parameter that made the vehicle not follow the safe trajectories as desired. |
format |
Other/Unknown Material |
author |
Johansson, Daniel |
author_facet |
Johansson, Daniel |
author_sort |
Johansson, Daniel |
title |
Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
title_short |
Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
title_full |
Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
title_fullStr |
Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
title_full_unstemmed |
Dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
title_sort |
dynamic path planning for collision avoidance in a robotized framework for autonomous driving verification |
publisher |
Lunds universitet/Institutionen för reglerteknik |
publishDate |
2019 |
url |
http://lup.lub.lu.se/student-papers/record/8986376 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
http://lup.lub.lu.se/student-papers/record/8986376 ISSN: 0280-5316 |
_version_ |
1772818605044400128 |