Trajectory Penetration Characterization for Efficient Vehicle Selection in HD Map Crowdsourcing

In this article, we investigate the worker (i.e., vehicle) selection problem in vehicle-based crowdsourcing (VBC), where vehicles in a specific area are recruited by the crowdsourcing platform to collect geographical information in real driving scenarios for autonomous driving. Given a limited recruitment budget, we formulate a cumulative platform utility maximization problem (CMP) to obtain the optimal worker set. The CMP is unsolvable directly as the platform has no prior information of workers at the initial stage (also known as “cold start”) and the cost of collecting all workers' information is prohibitive. To solve the problem, we first conduct a comprehensive data analytics on two real-world vehicle traces and obtain two crucial observations: 1) trajectory of individual vehicle is highly uncertain that it is difficult to make accurate prediction and 2) the overall distribution of vehicular trajectory penetration (measured by collection quantity and coverage) has a diurnal pattern and varies with weekly periodicity. Inspired by the insights, we propose the performance transfer-based online worker selection (POSE) scheme, which works independently from trajectory prediction with two components, i.e., transfer learning-based performance estimation and online worker selection (OWS). Based on the diurnal pattern, the former component collects a short-period trajectory penetration data of vehicles for model fitting, which can output a specific numerical distribution. With the fitting model, we can identify and select vehicles with high trajectory penetration at the initial stage to cope with the “cold start” problem. Then, we map the worker selection problem into a multiarmed bandit problem and develop upper confidence bound-based approach to solve it. Extensive trace-driven simulations are carried out and the results demonstrate the efficiency of POSE in terms of cumulative platform utility.