Parameterisation of lane-change scenarios from real-world data

TL;DR

This paper identifies key parameters from real-world data to model lane-change scenarios for autonomous vehicle testing, enabling realistic simulation and efficient scenario generation with risk assessment.

Contribution

It introduces a method to extract meaningful lane-change parameters from real-world data for realistic scenario modeling in AV testing.

Findings

Proposed parameters accurately model real-world lane-change scenarios

Adding disturbances creates diverse scenarios for testing

RMSE validates the scenario generation against real data

Abstract

Recent Autonomous Vehicles (AV) technology includes machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant crucial road situations, it can reduce the effort required in testing. Encoding real-world traffic participants' behaviour is essential to efficiently assess the System Under Test (SUT) in scenario-based testing. So, it is necessary to capture the scenario parameters from the real-world data that can model scenarios realistically in simulation. The primary emphasis of the paper is to identify the list of meaningful parameters that adequately model real-world lane-change scenarios. With these parameters, it is possible to build a parameter space capable of generating a range of challenging scenarios for AV testing efficiently. We validate our approach using Root Mean Square Error(RMSE) to compare the scenarios generated using the proposed parameters against the real-world trajectory data. In addition to that, we demonstrate that adding a slight disturbance to a few scenario parameters can generate different scenarios and utilise Responsibility-Sensitive Safety (RSS) metric to measure the scenarios' risk.