Deep Predictive Autonomous Driving Using MultiAgent Joint Trajectory Prediction and Traffic Rules Autonomous driving is a challenging problem because the autonomous vehicle must understand complex and dynamic environment. This understanding consists of predicting future behavior of nearby vehicles and recognizing predefined rules. It is observed that not all rules have equivalent values, and the priority of the rules may change depending on the situation or the driverxe2x80x99s driving style. In this work, we jointly reason both a future trajectories of vehicles and degree of satisfaction of each rule in the deep learning framework. Joint reasoning allows modeling interactions between vehicles, and leads to better prediction results. A rule is represented as a signal temporal logic (STL) formula, and a robustness slackness, a margin to the satisfaction of the rule, is predicted for the both autonomous and other vehicle, in addition to future trajectories. Learned robustness slackness decides which rule should be prioritized for the given situation for the autonomous vehicle, and filter out nonvalid predicted trajectories for surrounding vehicles. The predicted information from the deep learning framework is used in model predictive control (MPC), which allows the autonomous vehicle navigate efficiently and safely. We test the feasibility of our approach in publicly available NGSIM datasets. Proposed method shows a driving style similar to the human one and considers the safety related to the rules through the future prediction of the surrounding vehicles.

Jointly Learnable Behavior and Trajectory Planning for SelfDriving Vehicles The motion planners used in selfdriving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior planner, which handles highlevel decisions and produces a coarse trajectory, and trajectory planner that generates a smooth, feasible trajectory for the duration of the planning horizon. These planners, however, are typically developed separately, and changes in the behavior planner might affect the trajectory planner in unexpected ways. Furthermore, the final trajectory outputted by the trajectory planner might differ significantly from the one generated by the behavior planner, as they do not share the same objective. In this paper, we propose a jointly learnable behavior and trajectory planner. Unlike most existing learnable motion planners that address either only behavior planning, or use an uninterpretable neural network to represent the entire logic from sensors to driving commands, our approach features an interpretable cost function on top of perception, prediction and vehicle dynamics, and a joint learning algorithm that learns a shared cost function employed by our behavior and trajectory components. Experiments on realworld selfdriving data demonstrate that jointly learned planner performs significantly better in terms of both similarity to human driving and other safety metrics, compared to baselines that do not adopt joint behavior and trajectory learning.