Context and Intention Aware Planning for Urban Driving We present a novel autonomous driving system which uses the road contextual information and intentions of other road users for urban driving. Unlike highways, urban environments require the drivers to follow traffic signs and signals while using their best judgment for anomalous situations. In such scenarios, a selfdriving car needs to understand and take into account the uncertainties in the environment to plan and decide its action accordingly. Our planner models the intentions of the surrounding vehicles leveraging a neural network, and integrates the road contextual information to reduce its environment uncertainties and also speed up the decision making process. We validate our planner in simulation and in a real urban environment. Our experimental results show that integrating intention inference and road contextual information for prediction, planning and decision making help improve safety and efficiency of our autonomous driving system.

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.