Assessment of Automated Driving Systems using real-life scenarios
conference paper
More and more Advanced Driver Assistance Systems (ADAS) are entering the market for improving both safety and comfort by assisting the driver with their driving task. An important aspect in developing future ADAS and Automated Driving Systems (ADS) is testing and validation. Validating the failure rate of an ADS requires so many operational hours that testing in real time is almost impossible. One way to reduce this test load is virtual testing or hardware in-The-loop testing. The major challenge is to create realistic test cases that closely resemble the situation on the road. We present a way to use data of naturalistic driving to generate test cases for Monte-Carlo simulations of ADS. Because real-life data is used, the assessment allows to draw conclusions on how the ADS would perform in real traffic. The method, developed in EU AdaptIVe, is demonstrated by testing an Adaptive Cruise Control (ACC) system in scenarios where the predecessor of the ego vehicle is braking. We show that the probability of the occurrence of unsafe situations with the ACC system can be accurately and efficiently determined.
Topics
Adaptive control systemsAdvanced driver assistance systemsAutomobile driversCruise controlDecelerationFailure analysisIntelligent vehicle highway systemsLoad testingMonte Carlo methodsVehiclesVirtual realityAcc systemsAutomated driving systemsDriving tasksFailure rateHardware-in-the-loop testingReal life dataReal trafficVirtual testingAdaptive cruise control
TNO Identifier
780718
ISBN
9781509048045
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
7995782
Source title
28th IEEE Intelligent Vehicles Symposium, IV 2017. 11 June 2017 through 14 June 2017
Pages
589-594
Files
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