Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers

Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with "conservative" or "average" driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning 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 and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user's preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user's own driving behavior.