• Transport Research
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The rapid adoption of the vehicles and their on-board sensors as a primary means of transportation make them natural candidates for the outsourcing of data collection. However, vehicles mobility patterns tend to cluster into specific regions such as highways and popular roads, that makes their utilization difficult for data collection in isolated regions with low density traffic. We tackle this problem by proposing a probabilistic incentive mechanism for Vehicular Crowdsensing (VCS) that encourages vehicles to deviate from their pre-planned trajectories in order to visit and collect data from the isolated places. Our proposed framework is able to handle asynchronous vehicles. Also, vehicles consider the traffic holistically to find more profitable routes. By using a realistic vehicular movement data set (UBER movement), open-street maps (OSM) and SUMO vehicular traffic simulator, we show our algorithm significantly outperforms traditional approaches for trajectory generation in terms of spatial and temporal coverage, road utilization, and average participant utility.

In this article, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples from regions otherwise unreachable by originally planned routes. We model this problem as a non-cooperative game in which a set of AVs equipped with sensors are the players and their trajectories are the strategies. Thus, our solution corresponds to a model in which expected individual utility drives the mobility decision of participants. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show our algorithm significantly outperforms traditional approaches for trajectory generation. In particular, our performance evaluation shows a significant lift in crowdsourcer coverage, road utilization, and average participant utility.