Modeling, Learning and Prediction of Longitudinal Behaviors of HumanDriven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control Understanding the behaviors of humandriven vehicles such as acceleration and braking are critical for the safety of the nearfuture mixed transportation systems which involve both automated and humandriven vehicles. Existing approaches in modeling human driving behaviors including drivermodelbased 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.

Continuous Deep Maximum Entropy Inverse Reinforcement Learning using online POMDP A vehicle navigating in an urban environment must obey traffic rules by properly setting its speed, such as staying below the road speed limit and avoiding collision with other vehicles. This is presumably the scenario that autonomous vehicles will face: they will share the traffic roads with other vehicles (autonomous or not), cooperatively interacting with them. In other words, autonomous vehicles should not only follow traffic rules, but should also behave in such a way that resembles other vehicles behavior. However, manually specification of such behavior is a timeconsuming and errorprone task, since driving in urban roads is a complex task, which involves many factors. This paper presents a multitask decision making framework that learns an expert driveru0027s behavior driving in an urban scenario containing traffic lights and other vehicles. For this purpose, Inverse Reinforcement Learning (IRL) is used to learn a reward function that explains the expert driveru0027s behavior. Most IRL approaches require solving a Markov Decision Process (MDP) in each iteration of the algorithm to compute the optimal policy given the current rewards. Nevertheless, the computational cost of solving an MDP is high when considering large state spaces. To overcome this issue, the optimal policy is estimated by sampling trajectories in regions of the space with higher rewards. To do so, the problem is modeled as a continuous Partially Observed Markov Decision Process (POMDP), in which the intentions of other vehicles are only partially observed. An online solver is employed in order to sample trajectories given the current rewards. The efficiency of the proposed framework is demonstrated through simulations, showing that the controlled vehicle is be able to mimic an expert driveru0027s behavior.