scholarly journals Dynamic and Safe Path Planning Based on Support Vector Machine among Multi Moving Obstacles for Autonomous Vehicles

2013 ◽  
Vol E96.D (2) ◽  
pp. 314-328 ◽  
Author(s):  
Quoc Huy DO ◽  
Seiichi MITA ◽  
Hossein Tehrani Nik NEJAD ◽  
Long HAN
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Autonomous Vehicles ◽  
Support Vector ◽  
Moving Obstacles ◽  
Safe Path

scholarly journals Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 381 ◽  
Author(s):  
Yaping Liao ◽  
Junyou Zhang ◽  
Shufeng Wang ◽  
Sixian Li ◽  
Jian Han
Keyword(s):  
Support Vector Machine ◽  
Autonomous Vehicles ◽  
Injury Severity ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Support Vector ◽  
Severity Prediction ◽  
Emergency Decision ◽  
The Impact ◽  
Crash Injury Severity

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

Path planning using a Multiclass Support Vector Machine

2016 ◽  
Vol 43 ◽  
pp. 498-509 ◽  
Author(s):  
Néstor Morales ◽  
Jonay Toledo ◽  
Leopoldo Acosta
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Support Vector ◽  
Multiclass Support Vector Machine

scholarly journals Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 77
Author(s):  
Luís Carlos Santos ◽  
André Silva Aguiar ◽  
Filipe Neves Santos ◽  
António Valente ◽  
Marcelo Petry
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Support Vector ◽  
Topological Map ◽  
Agricultural Robots ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Two Stages ◽  
Computational Resources ◽  
Processing Techniques

Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot’s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution—called AgRoBPP-bridge—to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.

Collision Probability based Safe Path Planning for Mobile Robots in Changing Environments

2012 ◽  
Vol 197 ◽  
pp. 401-408 ◽  
Author(s):  
Hong Liu ◽  
Chuang Qi Wang
Keyword(s):  
Path Planning ◽  
Computer Aided Design ◽  
State Of The Art ◽  
Collision Probability ◽  
Changing Environments ◽  
Moving Obstacles ◽  
On Line ◽  
Path Planner ◽  
Aided Design ◽  
Safe Path

Automatic path planning has many applications in robotics, computer-aided design(CAD) and industrial manipulation. The property of safety is vital but seldom taken into consideration by typical path planning. In this paper, collision probability is introduced as an evaluation of crowd degree of environments to get a safer path. The smaller collision probability a node has, the more possibly the node can be extended. Meanwhile, the in/out degree of a node is limited to prevent some nodes to be extended excessively. Through evaluating collision probability on-line, a safe path planning based on DRRTs, called Safe-DRRT, is proposed to provide a path not only feasible but also safe. Finally, a path planner is implemented with Safe-DRRT as a guidance and a local planner. In plentifully crowded experiments with moving obstacles, the proposed method has demonstrated to be competent compared to the state of the art.

A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments

2017 ◽  
Vol 44 (4) ◽  
pp. 406-415 ◽  
Author(s):  
Jiajia Chen ◽  
Wuhua Jiang ◽  
Pan Zhao ◽  
Jinfang Hu
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Planning Method ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Planning Problem ◽  
Fuzzy Support Vector Machine ◽  
Content Type

Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm. Findings The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance. Originality/value This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

scholarly journals A Novel Path Planning Method for Articulated Road Roller Using Support Vector Machine and Longest Accessible Path With Course Correction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 182784-182795 ◽  
Author(s):  
Xu Tong ◽  
Chen Siwei ◽  
Wang Dong ◽  
Wu Ti ◽  
Xu Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Planning Method ◽  
Support Vector
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