An event-triggered real-time motion planning strategy for autonomous vehicles

Abstract

Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

Behavior Planning for Autonomous Driving: Methodologies, Applications, and Future Orientation

“…A Hierarchal Finite State Machine (HFSM) algorithm is implemented by [1]. In which they were able to handle the collisions using a cost assessment model to select the appropriate behavior.…”

“…Autonomous vehicles are highly technological products with embedded electronics and intelligent algorithms [1]. It aims to avoid the problem of collisions that are made due to human errors, save energy, and allow passengers to have comfortable traveling experiences [2]- [5].…”

“…The autonomous driving planning goal is to find suitable control commands to drive a vehicle from a certain start point to a goal point [12]. It also aims to establish its task safely and efficiently [1]. The planning and control module consists of four subparts which are mission planning, Behavior Planning (BP), local planning, and vehicle control as shown in fig.…”

An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

“…Rule-based path planning strategies are widely used in autonomous driving systems [4] and the planning results are strictly constrained by the planning process, i.e., if the inputs are unchanged, the output path remains unchanged [5,6]. The driving path can be obtained using the curve-fitting-based planning strategies through fitting functions.…”

“…This work aims to provide a flexible, accurate, and efficient path planning strategy for autonomous vehicles traveling on half-structured roads: (1) bringing higher efficiency compared to traditional sampling strategies, especially in vast areas [4]; (2) bringing more flexibility compared to state machine-based planning strategies and avoiding unintended hazards due to fault transmission [15]; and (3) bringing a higher degree of interpretability compared to numerical optimization strategies [16], where the output paths are filtered from within the path candidates.…”