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Real-Time Behaviour Planning and Highway Situation Analysis Concept with Scenario Classification and Risk Estimation for Autonomous Vehicles

1,*,†
,
1,*,†
and
2,†
1
Department of Automation and Applied Informatics, Budapest University of Technology and Economics, 1117 Budapest, Hungary
2
Knorr-Bremse Fékrendszerek Kft., Commercial Vehicle Systems Division, Advanced Engineering, 1119 Budapest, Hungary
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 1 December 2018 / Revised: 28 December 2018 / Accepted: 11 January 2019 / Published: 15 January 2019
(This article belongs to the Special Issue Advances in Modeling, Control and Safety of Vehicle Systems)
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Abstract

The development of autonomous vehicles is one of the most active research areas in the automotive industry. The objective of this study is to present a concept for analysing a vehicle’s current situation and a decision-making algorithm which determines an optimal and safe series of manoeuvres to be executed. Our work focuses on a machine learning-based approach by using neural networks for risk estimation, comparing different classification algorithms for traffic density estimation and using probabilistic and decision networks for behaviour planning. A situation analysis is carried out by a traffic density classifier module and a risk estimation algorithm, which predicts risks in a discrete manoeuvre space. For real-time operation, we applied a neural network approach, which approximates the results of the algorithm we used as a ground truth, and a labelling solution for the network’s training data. For the classification of the current traffic density, we used a support vector machine. The situation analysis provides input for the decision making. For this task, we applied probabilistic networks. View Full-Text
Keywords: autonomous driving; machine learning; neural networks; risk estimation; Bayesian networks; behaviour planning autonomous driving; machine learning; neural networks; risk estimation; Bayesian networks; behaviour planning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dávid, B.; Láncz, G.; Hunyady, G. Real-Time Behaviour Planning and Highway Situation Analysis Concept with Scenario Classification and Risk Estimation for Autonomous Vehicles. Designs 2019, 3, 4.

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