Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments

Shen, Macheng; Zhao, Ding; Sun, Jing; Peng, Huei

doi:10.48550/arxiv.1702.05792

Cited by 7 publications

(12 citation statements)

References 15 publications

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“…The third assumption has been experimentally verified in [12] under open sky conditions. Consider a network of connected vehicles consisting of N vehicles.…”

Section: Derivation Of the Cmm Errormentioning

confidence: 92%

“…Each sample value for the orthogonal road case is calculated through two approaches. One approach is the analytic formula (12), and the other approach is an importance sampling Monte Carlo integration where the proposal distribution is a two-dimensional uniform distribution. Besides, the number of vehicles in each direction is the same.…”

Section: B Uniformly Distributed Random Road Directionsmentioning

confidence: 99%

“…These works present promising improvements on GNSS localization, while most of them utilize additional information from different sources. Improving the localization accuracy of these widespread GNSS without incurring additional hardware and infrastructure costs has motivated recent research activities on Cooperative Map Matching (CMM) [9], [12], [10]. CMM has been shown capable of improving Global Navigation Satellite System (GNSS) positioning of a group of connected vehicles through estimation and correction of the GNSS common localization error.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

Shen

Sun

Zhao

2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. It has been shown, in our previous work, that the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints. While further error reduction is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This work presents a theoretical study that establishes a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule. The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the Safety Pilot Model Deployment and Pillar dataset of Ann Arbor traffic flow collected over three years period. The contributions of this work include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.

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“…The third assumption has been experimentally verified in [12] under open sky conditions. Consider a network of connected vehicles consisting of N vehicles.…”

Section: Derivation Of the Cmm Errormentioning

confidence: 92%

Section: B Uniformly Distributed Random Road Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

Shen

Sun

Zhao

2018

IEEE Trans. Intell. Transport. Syst.

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“…Various techniques have been used to reduce GNSS localization error such as precise point positioning (PPP) [1], real time kinematics (RTK) [2] and sensor fusion [3]. Nonetheless, improving the localization accuracy of the widespread GNSS without incurring additional hardware and infrastructure costs has motivated recent research activities on cooperative GNSS localization [4] and cooperative map matching (CMM) [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…al. [5] and a Bayesian approach based on a Rao-Blackwellized Particle Filter in our previous work [6], [7]. One common feature of these two CMM algorithms is that the probabilistic property of the GNSS error is utilized.…”

Section: Introductionmentioning

confidence: 99%

Optimization of vehicle connections in V2V-based cooperative localization

Shen

Sun

Zhao

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy. It has been shown to reduce GNSS error from several meters to sub-meter level by matching the biased GNSS positioning of four vehicles to a digital map with road constraints in our previous work. While further error reduction is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership of the CMM affects the performance of the GNSS-based localization results need to be addressed to provide guidelines for design and optimization of the vehicle network. The quantitative relationship between the estimation error and the road constraints has to be systematically investigated to provide insights. In this work, a theoretical study is presented that aims at developing a framework for quantitatively evaluating effects of the road constraints on the CMM accuracy and for eventual optimization of the CMM network. More specifically, a closed-form expression of the CMM error in terms of the road angles and GNSS error is first derived based on a simple CMM rule. Then a Branch and Bound algorithm and a Cross Entropy method are developed to minimize this error by selecting the optimal group of vehicles under two different assumptions about the GNSS error variance.

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