Abstract
This paper presents and evaluates a unified cooperative perception framework that employs vehicle-to-vehicle (V2V) connectivity. At the core of the framework is a decentralized data association and fusion process that is scalable with respect to participation variances. The evaluation considers the effects of the communication losses in the ad-hoc V2V network and the random vehicle motions in traffic by adopting existing models along with a simplified algorithm for individual vehicle’s on-board sensor field of view. Furthermore, a multi-target perception metric is adopted to evaluate both the errors in the estimation of the motion states of vehicles in the surrounding traffic and the cardinality of the fused estimates at each participating node/vehicle. The extensive analysis results demonstrate that the proposed approach minimizes the perception metric for a much larger percentage of the participating vehicles than a baseline approach, even at modest participation rates, and that there are diminishing returns in these benefits. The computational and data traffic trade-offs are also analyzed.
Published Version
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