Enhancing GPS with Lane-level Navigation to Facilitate Highway Driving

Abstract

Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle’s lane position and to advise the driver of lane switching decisions. Google Maps stores apriori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle’s lane position. The system leverages smart phones’ computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems.

Existing System

Energy-efficient lane detection system.

Proposed System

In this paper, we address the problem of achieving lane level navigation using smart phones in order to help people with route planning when they drive on highways. In other words, our goal is to let a user’s smart phone determine the current lane-level position and track any lane change of the vehicle. Furthermore, we can extend our proposed system to a server/client based model to collect the lane information and abnormal highway situations from clients and advise lane changes through the server to facilitate highway driving. Humans are able to identify their lane-level positions on highways through vision, which inspired us to make use of the embedded rear camera of the smart phones to capture the road view. We also take advantage of the fact that a driver tends to mount his/her phone on the windshield or the dashboard when using navigation applications, which grants the rear camera of the phone a good spot to have quality views of the road ahead. Additionally, smart phones’ inertial sensors can help detect the vehicle’s physical displacement when the driver changes lanes.

Architecture

System Overview