Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota
The use of rumble strips on roads has proven to be an effective means of providing drivers lane departure warning
(LDW). However, rumble strips require an infrastructure and do not exist on a majority of roadways. Furthermore,
rumble strips present a difficult issue of where to establish the rumble-strip distance threshold. To develop an
effective virtual rumble-strip LDW system where the rumble-strip threshold is allowed to vary according to the risk
of the vehicle departing the road, it is essential to know the vehicle’s lateral characteristics; in particular, the
vehicle’s lateral position and speed. In this report, we use image processing via an in-vehicle camera to estimate the
vehicle’s lateral position and speed. The lateral position is estimated by determining the vehicle’s heading angle via
a homography and the Lucas-Kanade optical flow techniques; while the lateral speed is determined via the heading
angle and the vehicle’s On Board Diagnostic (OBD)-II forward speed data access. The detail of our approach is
presented in this report together with our findings. Our approach will only need the minimal set of information to
characterize the vehicle lateral characteristics, and therefore, makes it more feasible in a vehicle application.
Estimation of Vehicle's Lateral Position via the Lucas-Kanade Optical Flow Method.
Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota.
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