Amateur

Abstract

This paper presents Amateur, an augmented reality based vehicle navigation system using commodity smart phones. Amateur reads the navigation information from a digital map, matches it into live road condition video captured by smart phone, and directly annotates the navigation instructions on the video stream. The Amateur design entails two major challenges, including the lane identification and the intersection inference so as to correctly annotate navigation instructions for lane-changing and intersection-turning. In this paper, we propose a particle filter based design, assisted by inertial motion sensors and lane markers, to tolerate incomplete and even erroneous detection of road conditions. We further leverage traffic lights as land markers to estimate the position of each intersection to accurately annotate the navigation instructions. We develop a prototype system on Android mobile phones and test our system in a total number of more than 300km travel distance on different taxi cabs in a city. The evaluation results suggest that our system can timely provide correct instructions to navigate drivers. Our system can identify lanes in 2s with 92.7% accuracy and detect traffic lights with 95.29% accuracy. Overall, the accuracy of the navigation signs placement is less than 105 pixels on the screen throughout the experiments. The feedback from 50 taxi drivers indicates that Amateur provides an improved experience compared to traditional navigation systems.