Safe Path Planning with Multi-Model Risk Level Sets

This paper investigates the safe path planning problem for an autonomous vehicle operating in unstructured, cluttered environments. While some objects may be accurately with canonical perception algorithms, other objects and clutter may be harder to track. We present an approach that combines two methods of risk assessment: for objects with reliable tracking, we use a Gaussian Process (GP) regulated risk map to describe the risk map information; for unknown objects that we fail to accurately track, we compute a Dynamic Risk Density (DRD) from the overall occupancy and velocity field from LiDAR scan snapshots. Several methods are proposed for combining the GP risk map and DRD, and the resultant hybrid risk map is used for the proposed safe path planning algorithm. Experimental results on an autonomous buggy show that the hybrid risk map is able to yield a safe path planner to navigate the autonomous testbed within the cluttered environments.

Authors

• Zefan Huang
• Wilko Schwarting
• Alyssa Pierson
• Hongliang Guo 0003
• Marcelo H. Ang
• Daniela Rus

Keywords

• Roads
• Urban areas
• Path planning
• Planning
• Risk management
• Vehicle dynamics
• Autonomous vehicles
• Safe Path
• Safe Path Planning
• Navigation
• Flow Velocity
• Gaussian Process
• Risk Map
• Risk Assessment Methods
• Cluttered Environments
• Object Detection
• Risk Model
• Safety Net
• Model Predictive Control
• Risk Value
• Density Field
• Object Tracking
• Convex Combination
• Combined Risk
• Environmental Point
• Residential Density
• Static Obstacles
• Safe Region
• Dynamic Obstacles
• Feasible Path
• Simultaneous Localization And Mapping
• Dijkstra’s Algorithm
• Unstructured Environments
• Environmental Density
• Environment Map