2022/03/14 by Dhanoop Karunakaran, Julie Stephany Berrío, Karunakaran, Dhanoop +7
Computer Science · Decision Sciences · Engineering · #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Data mining #Engineering #FOS: Computer and information sciences #I.6.4 #Machine learning #Probabilistic logic #Reuse #Robotics (cs.RO) #Scenario testing #Simulation Techniques and Applications #Software deployment #Software engineering #Traffic Prediction and Management Techniques #cs.RO
paper · pdf · doi:10.48550/arxiv.2203.07521
Submitted to IEEE IV 22 conference
arxiv created 2022/03/14 · openalex publication_date 2022/03/14 · arxiv updated 2022/03/16 · openalex created_date 2022/04/03 · openalex updated_date 2026/08/05
Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite many safety challenges yet to be resolved. The modern autonomous vehicles will undoubtedly include machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. Road testing is essential before the deployment, but scenarios are repeatable, and it's hard to collect challenging events. Exploring numerous, diverse and crucial scenarios is a time-consuming and expensive approach. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant critical road situations, it can reduce the effort required in testing. The scenario-based testing in simulation requires the realistic behaviour of the traffic participants to assess the System Under Test (SUT). It is essential to capture the scenarios from the real world to encode the behaviour of actual traffic participants. This paper proposes a novel scenario extraction method to capture the lane change scenarios using point-cloud data and object tracking information. This method enables fully automatic scenario extraction compared to similar approaches in this area. The generated scenarios are represented in OpenX format to reuse them in the SUT evaluation easily. The motivation of this framework is to build a validation dataset to generate many critical concrete scenarios. The code is available online at https://github.com/dkarunakaran/scenarioextractionframework.