Journal of Intelligent & Robotic Systems

, Volume 72, Issue 3, pp 559–590

Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization

Article

DOI: 10.1007/s10846-013-9817-7

Cite this article as:
Kala, R. & Warwick, K. J Intell Robot Syst (2013) 72: 559. doi:10.1007/s10846-013-9817-7

Abstract

The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.

Keywords

Unmanned ground vehicles Dijkstra’s algorithm Optimization Robotic motion planning Nonholonomic constraints Autonomous vehicles 

Mathematics Subject Classification (2010)

68T40 

Supplementary material

10846_2013_9817_MOESM1_ESM.zip (66 kb)
Online Resouce 1: Path traced by single vehicle for various scenarios (ZIP 66.0 KB)
10846_2013_9817_MOESM2_ESM.zip (172 kb)
Online Resouce 2: Path traced by two vehicles for various scenarios (ZIP 171 KB)

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Systems EngineeringUniversity of ReadingWhiteknightsUK