Highway Situation Analysis with Scenario Classification and Neural Network based Risk Estimation for Autonomous Vehicles

Dávid, Bence; Láncz, Gergő; Hunyady, Gergely

doi:10.1109/sami.2019.8782729

Cited by 5 publications

(2 citation statements)

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“…Dávid et al [71] use artificial intelligence for the online determination of the current risk and classification of the current driving situation. The focus of the authors is on the methodology, and the classified situations are not further used for the safety assessment.…”

Section: Scenario Classificationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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Section: Scenario Classificationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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“…David, Lancz, and Hunyady investigated the use of neural networks for real-time risk estimation and different classification algorithms for traffic situation classification. In this context, the risk associated with rapid maneuvers of the ego vehicle is identified based on the probability of a collision (calculated according to Time to Collision (TTC)) and the severity resulted from that collision [17].…”

Section: Related Workmentioning

confidence: 99%

Don’t Go That Way! Risk-Aware Decision Making for Autonomous Vehicles

2020

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Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles. Accurately predicting risk or potentially risky situations is critical for the safe operation of autonomous vehicles. In our previous work, we showed that risk could be characterized by two components: 1) the probability of an undesirable outcome and 2) an estimate of how undesirable the outcome is (loss). This paper is an extension to our previous work. In this paper, using our trained deep reinforcement learning model for navigating around crowds, we developed a risk-based decision-making framework for the autonomous vehicle that integrates the high-level risk-based path planning with the reinforcement learning-based low-level control. We evaluated our method in a high-fidelity simulation such as CARLA. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.

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Extensible Highway Simulation Environment Supporting the Research of Situation Analysis and Behaviour Planning for Autonomous Vehicles

Dávid

2019

2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on R

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Highway Situation Analysis with Scenario Classification and Neural Network based Risk Estimation for Autonomous Vehicles

Cited by 5 publications

References 9 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Don’t Go That Way! Risk-Aware Decision Making for Autonomous Vehicles

Extensible Highway Simulation Environment Supporting the Research of Situation Analysis and Behaviour Planning for Autonomous Vehicles

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