Validating the safety of Autonomous Vehicles (AV s) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corner cases in simulation-based scenario testing, the safety of AVs can be improved. However, the creation of corner case scenarios including multiple agents is non-trivial. Our approach allows engineers to generate novel, realistic corner cases based on historic traffic data and to explain why situations were safety -critical. In this paper, we introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel. The structure of PLGs is learnt directly from spatio-temporal traffic data. The graph model represents the actions of the drivers in response to a given state in the form of a probabilistic policy. We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AV s.
Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs
2023-09-24
778466 byte
Conference paper
Electronic Resource
English
Explaining high-occupancy-toll lane use
Online Contents | 2001
|Accident Lane Prediction Using Probabilistic Inference
IEEE | 2019
|LANE CHANGE PLAN GENERATING DEVICE, LANE CHANGE PLAN GENERATING METHOD
European Patent Office | 2016
|Probabilistic framework for ego-lane determination
IEEE | 2019
|