Evaluation of Lane Departure Correction Systems Using a Stochastic Driver Model
For automotive safety engineers, this provides a data-driven evaluation method for lane departure correction systems, though it is incremental as it applies existing modeling techniques to a specific application.
This paper proposes a low-cost framework for testing lane departure correction systems using 529,096 real-world lane departure events. A stochastic driver model with eight variables was developed, and simulations showed the method can effectively evaluate system performance by comparing lateral departure areas with and without correction.
Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data. First, 529,096 lane departure events were extracted from the Safety Pilot Model Deployment (SPMD) database collected by the University of Michigan Transportation Research Institute. Second, a stochastic lane departure model consisting of eight random key variables was developed to reduce the dimension of the data description and improve the computational efficiency. As such, we used a bounded Gaussian mixture model (BGM) model to describe drivers' stochastic lane departure behaviors. Then, a lane departure correction system with an aim point controller was designed, and a batch of lane departure events were reproduced from the learned stochastic driver model. Finally, we assessed the developed evaluation approach by comparing lateral departure areas of vehicles between with and without correction controller. The simulation results show that the proposed method can effectively evaluate lane departure correction systems.