Multi-Grid based decision making at Roundabout for Autonomous Vehicles

Maintaining safety in roundabouts is crucial in autonomous vehicles (AV) controlling and path planning. The number of vehicles in a roundabout at a time and the rules they must obey can make it a very complex traffic environment. Before an AV starts entering a roundabout, oncoming vehicles must be identified, and it could be done by determining their position, speed and direction. To address that, this paper extends some of our previous works in AV decision making in roundabouts and proposes a multi-grid-based image processing approach using multiple cameras (MGC). Particularly, it utilises a fine grid to determine speed and direction of approaching vehicles, whilst the position is evaluated using a larger grid. Besides, using multiple cameras allows the system to mimic the real drivers' view and perception in approaching the real roundabouts, hence a human-like decision can be made. Three different classifiers including SVM, ANN and kNN were examined using 460 video clips of real roundabout-drive circumstances. The highest score was obtained by SVM at nearly 97% accuracy rate, with the making decision time is only around one second. That promising result indicates the applicability of the MGC system in real traffic situations.