Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control

Abstract

Understanding the behaviors of human-driven vehicles such as acceleration and braking are critical for the safety of the near-future mixed transportation systems which involve both automated and human-driven vehicles. Existing approaches in modeling human driving behaviors including driver-model-based approaches and heuristic approaches have issues in either model accuracy or scalability limitation to new situations. To address these issues, this paper proposes a new inverse model predictive control (IMPC) based approach to model longitudinal human driving behaviors. The approach incorporates the internal decision making process of humans, and achieves better predicting accuracy and improved scalability to different situations. The modeling, learning, and prediction of longitudinal human driving behaviors using the proposed IMPC approach are presented. Experimental results validate the effectiveness and advantages of the approach.