Safe Path Planning with Gaussian Process Regulated Risk Map

Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.

Authors

• Hongliang Guo 0003
• Zehui Meng
• Zefan Huang
• Wei Kang Leong
• Ziyue Chen
• Malika Meghjani
• Marcelo H. Ang
• Daniela Rus

Keywords

• Heuristic algorithms
• Simulation
• Government
• Gaussian processes
• Path planning
• Safety
• Driver behavior
• Vehicle dynamics
• Autonomous vehicles
• Accidents
• Gaussian Process
• Risk Map
• Safe Path
• Safe Path Planning
• Object Detection
• Detection Module
• Object Tracking
• Tracking Module
• Shortest Path
• Constant Velocity
• Risk Value
• Linear Velocity
• Simple Scenario
• Environmental Uncertainty
• Linear Case
• Environmental Point
• Update Process
• Simultaneous Localization And Mapping
• Dynamic Obstacles
• Static Obstacles
• Rapidly-exploring Random Tree
• Conditional Value At Risk
• Obstacle Location
• True Location
• Value At Risk
• 2D Environment
• Dijkstra’s Algorithm
• Online Update