A Simulation Environment for Analysis and Optimization of Driver Models

Benderius, Ola; Markkula, Gustav; Wolff, Krister; Wahde, Mattias · 2011 · Crossref

DOI: 10.1007/978-3-642-21799-9_51

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Summary

This paper introduces a simulation environment designed for the evaluation and optimization of driver models, addressing the challenge of systematically tuning model parameters to match real-world driving data. The authors motivate this work by noting that while computer simulation is increasingly used in automotive research to complement costly or dangerous physical tests, meaningful results require accurate human driver models. Existing literature often lacks systematic methods for mapping driver model parameters to observed behavior. To bridge this gap, the presented environment integrates vehicle dynamics, traffic scenarios, and an optimization framework based on stochastic algorithms, such as genetic algorithms (GAs) and particle swarm optimization (PSO). The simulation environment, implemented in C#, features modular vehicle dynamics models ranging from simple kinematic bicycle models to full dynamics models including steering, powertrain, and braking systems. It also includes a traffic environment defined by splines and stationary objects, along with a data manager for handling time-series signals. A key feature is the optimization framework, which uses population-based algorithms to infer driver model parameters by minimizing an error measure between simulated outputs and observed data. The system runs approximately 50 times faster than real time on standard desktop hardware, facilitating rapid parameter evaluation. The authors validate the environment through two experiments. The first involves an emergency braking scenario using artificial data generated by a known driver model with four parameters. The optimization framework successfully recovered these parameters with high accuracy, whether inferring from brake pedal position or vehicle speed data, though the latter showed slightly larger variance due to the indirect mapping. The second experiment uses real data from a truck performing a double lane change on an icy test track. Due to incomplete GPS data, the analysis focused on steering wheel angle variations. The optimization identified two distinct, equally viable parameter sets: one representing early, soft steering and another representing late, violent steering. This suggests that real-world data may correspond to multiple valid driver behaviors rather than a single unique solution. The significance of this work lies in providing a tool for reliably inferring driver model parameters from limited observational data, a common constraint in driving studies. The authors emphasize the importance of avoiding overfitting, noting that the environment supports holdout validation, although it was not used in these specific examples due to data limitations. The ability to rapidly optimize driver models on standard hardware enables researchers to explore driver variability and improve the fidelity of simulations for active safety system testing and vehicle development.

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Topics

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• situational awareness
• braking response

Information type

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• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model