Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception

Abstract

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers.

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

“…Path planning is a mapping from perceptual space to behavioral space and the planning method is one of the research hotspots at present. There are a variety of path planning methods commonly used, such as the potential energy method [1], heuristic search algorithm [2], Dijkstra algorithm [3], LPA* algorithm (Life Planning A*) [4], Floyd algorithm [5], PRM algorithm [6], RRT algorithm [7], unit division method [8] and intelligent algorithm [9][10][11]. However, these path planning algorithms cannot satisfy global adjustment, real-time change and multi-obstacle avoidance at the same time.…”