ITSC 2024 Paper Abstract

Paper FrBT8.3

Zaker, Mahdieh (Newcastle University), Blom, Henk A.P. (Delft University of Technology), Soudjani, Sadegh (Max Planck Institute for Software Systems), Lavaei, Abolfazl (Newcastle University)

Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

Scheduled for presentation during the Regular Session "Advanced Vehicle Safety Systems III" (FrBT8), Friday, September 27, 2024, 14:10−14:30, Salon 16

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Keywords Advanced Vehicle Safety Systems, Other Theories, Applications, and Technologies, Simulation and Modeling

Abstract

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: formal modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-to-collision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.