Behavior Planning of Autonomous Cars with Social Perception Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area. To safely and efficiently drive in the presence of these uncertainties, the decisionmaking and planning modules of autonomous cars should intelligently utilize all available information and appropriately tackle the uncertainties so that proper driving strategies can be generated. In this paper, we propose a social perception scheme which treats all road participants as distributed sensors in a sensor network. By observing the individual behaviors as well as the group behaviors, uncertainties of the three types can be updated uniformly in a belief space. The updated beliefs from the social perception are then explicitly incorporated into a probabilistic planning framework based on Model Predictive Control (MPC). The cost function of the MPC is learned via inverse reinforcement learning (IRL). Such an integrated probabilistic planning module with socially enhanced perception enables the autonomous vehicles to generate behaviors which are defensive but not overly conservative, and socially compatible. The effectiveness of the proposed framework is verified in simulation on an representative scenario with sensor occlusions.

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.