Bayesian Simultaneous Localization and Multi-Lane Tracking Using Onboard Sensors and a SD Map

TL;DR

This work presents a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles, and utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points.

Abstract

High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle's 6D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

Figures (1)

• Figure 1: OxTS vehicle trajectory (blue), estimated vehicle trajectory (red), estimated traffic lines (green) overlaid with Google Earth satellite image. The top figure illustrates the best traffic line estimation performance, followed by the bottom figure and the mid-figure.