Creating Semantic HD Maps From Aerial Imagery and Aggregated Vehicle Telemetry for Autonomous Vehicles

Abstract

We present a new framework for creating lane-level detailed HD-maps at scale for autonomous vehicles (AVs). In order to overcome scaling challenges of ground survey-based HD-map creation, we propose a number of innovations by leveraging three data sources: high-resolution aerial imagery, aggregated vehicle telemetry, and a navigation map. We first divide map creation problem into several categories based on lane configurations. The road category is predicted in a supervised setting using aerial imagery, which is pre-processed by using aggregated vehicle telemetry without supervision. We utilized the navigation map to guide the process along the road network and used aerial imagery and aggregated vehicle telemetry to extract lane level features at each step. We propose a multi-task convolutional neural network (CNN) to predict road polygons, road-way centerline, number of lanes, lane and road edges using both the aerial imagery and the corresponding aggregated vehicle telemetry. The predicted road features for each image are then stitched along a road segment to construct the road and lane edge polylines, which are then used to predict lane marking and road edge types in a sliding window fashion along the road segment. The extracted features are finally utilized to calculate higher level features for each point in the HD-map. Our experimental results show that the proposed framework works well, offering a flexible solution for creating HD-maps for AVs at scale.