A Two-Level Stochastic Model for the Lateral Movement of Vehicles Within Their Lane Under Homogeneous Traffic Conditions

TL;DR

This paper introduces a two-level stochastic model for simulating vehicle lateral movements within lanes, crucial for autonomous vehicle sensor perception and validation, with high accuracy and low computational cost.

Contribution

It presents a novel two-level stochastic approach combining a Markov model and noise model, validated with real data, for realistic lane-keeping behavior simulation.

Findings

Model produces realistic lateral offset profiles.

Simulation runs 10,000 times faster than real time.

Qualitative and quantitative evaluation shows promising results.

Abstract

The lateral position of vehicles within their lane is a decisive factor for the range of vision of vehicle sensors. This, in turn, is crucial for a vehicle's ability to perceive its environment and gain a high situational awareness by processing the collected information. When aiming for increasing levels of vehicle autonomy, this situational awareness becomes more and more important. Thus, when validating an autonomous driving function the representativeness of the submicroscopic behavior such as the lateral offset has to be ensured. With simulations being an essential part of the validation of autonomous driving functions, models describing these phenomena are required. Possible applications are the enhancement of microscopic traffic simulations and the maneuver-based approach for scenario-based testing. This paper presents a two-level stochastic approach to model the lateral movement of vehicles within their lane during road-following maneuvers under homogeneous traffic conditions. A Markov model generates the coarse lateral offset profile. It is superposed with a noise model for the fine movements. Both models are set up using real-world data. The evaluation of the model shows promising qualitative and quantitative results, the potential for enhancements and extreme low computation times (10000 times faster than real time).