Cooperative Vehicle Sensing and Obstacle Avoidance for Intelligent Driving Based on Bayesian Frameworks

Ma, Yuan; Zhang, Tingting; Tian, Xuanxuan

doi:10.1007/978-981-10-6571-2_69

Cited by 1 publication

(2 citation statements)

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“…It should be emphasized that in the literature of wireless sensor networks (WSNs), some target tracking algorithms [23][24][25] have been developed for simultaneous localization and tracking (SLAT). In [24], a Bayesian filtering framework was proposed to infer the joint posterior distribution of both target and multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Variational method [26] was used to approximate the joint state during the measurements correction stage. In [25], a dynamic non-parametric belief propagation (DNBP) method was proposed for cooperative vehicle sensing. However, most works in SLAT tend to track one target by using multiple static or moving sensors, thus, restricting their application to more complex scenarios where the number of targets can vary at different times.…”

Section: Introductionmentioning

confidence: 99%

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Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication

Chen

Wang

2020

Sensors

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The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.

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