Lane Departure Warning System using Road Reference Heading (RRH) Generated from Google Routes or Past Vehicle Trajectories

Abstract

Unintentional lane departure is a significant safety risk. Currently, available commercial lane departure warning systems use vision-based or GPS technology with lane-level resolution. These techniques have their own performance limitations and are complex and expensive to implement, inhibiting their widespread market penetration.Our previously developed LDWS system was based on a standard GPS receiver incorporating two algorithms to detect an unintentional lane departure. The first algorithm generated the Road Reference Heading (RRH) from a vehicle’s past trajectories, while the second algorithm compared the RRH of a given road with a vehicle’s current trajectory on that road for Lane Departure Detection (LDD) in real time. A significant limitation of the previously developed LDWS system is the dependency on past trajectories. A vehicle must travel on the road at least once in the past to use that trajectory for RRH generation to detect unintentional lane departures of a future trip on the same route. To avoid dependency on past trajectories, this work uses Google routes instead of past trajectories to extract the RRH of any given road with some enhancements to the LDD algorithm to detect lane departure more efficiently. We also compared the RRH generated from a Google route trajectory with that of a past trajectory and found both RRHs to be comparable indicating that our LDWS does not need to rely on RRH from past trajectories. To evaluate the accuracy of lane departure detection using Google RRH, we performed many field tests on a freeway. Our field test results show that our LDWS can accurately detect all lane departures on long straight sections of the freeway irrespective of whether the RRH was generated from a Google route or past trajectory. Due to its robustness and simplicity, we have also developed a smartphone app for this technique incorporating our modified RRH generation and LDD algorithms to detect a lane departure and issue a warning to the driver in real-time using an audible alarm. We have developed the app database structure and have completed programming the algorithms for the app. We are currently in the testing phase. The smartphone app is being prepared for both iOS and Android phones. However, the Android app will be available before the iOS app.