16 Personalized Lane Changes Using Subjective Risk-Sensitive Framework

Most of the current researches on autonomous vehicles’ control assume that all vehicles should have the same patterns of driving implemented, resulting in conservative or average systems. However, these results may not be acceptable to drivers who prefer a more aggressive style of driving, while extremely cautious drivers may consider the standard outputs to be too aggressive. In this chapter, we introduce risk-sensitive control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporate them into a receding-horizon controller. RSC uses a metalearning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user for subjective risk feedback. The estimator takes into account the individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and constraints. We test this approach using five-lane change scenarios, some safe and some risky, with 30 real drivers in a CARLA simulation environment. Based on both quantitative and qualitative evaluations, our experimental results demonstrate that the proposed framework can generate users’ preferred driving commands during lane changes, that is, commands associated with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user’s own driving behavior.