Abstract-We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework.

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Abstract-In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higherquality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs highquality trajectories and performs intelligent driving behaviors.

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In order to improve energy efficiency of transport systems, eco-driving strategies are studied world-widely. However, most literatures on eco-driving based on traditional traffic flow models, are greatly simplified, and can not evaluate the effects on detailed driving behaviors. By referring to robot motion planning approaches, in this research a microscopic vehicle model is developed and it can represent different driving behaviors, such as aggressive or conservative driving; a collision detection algorithm is proposed that takes O(1) time to check for a trajectory's collision, enabling realtime planning; and a traffic simulation system is developed by incorporating traffic rules, so that the driving behaviors such as observing or not observing traffic rules can also be represented. Experiments are conducted on the simulation platform, and the performance of different driving behaviors on travel time, mileage, comfort and eco is studied.