Toward Efficient Trajectory Planning based on Deterministic Sampling and Optimization

The solution of optimization-based planners depends heavily on a good initialization and their run-time is often non-deterministic, especially in dense obstacle fields. Sampling-based planning, whether probabilistic or deterministic, is a well-established method for exploring the search space. However, the downsides are also obvious: potentially intractable computational overhead, the curse of dimensionality and the sub-optimality due to discretization. Motivated by this observation, this paper introduces a real-time trajectory planning algorithm based on the combination of sampling and optimization approaches, which is applicable to autonomous vehicles operating in highly constrained environments. A maximum corridor width region and initial drivable path are firstly extracted from deterministic sampling. Then the initial path is further optimized through a splined-based quadratic programming and appended with a speed profile. This planner is scalable to both high-speed off-road scenarios and structured urban driving.