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Register a new userSemi-Interpenetrating Cooperative Localization in Connected Vehicle Networks
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Added by
Ding Zhao
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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We proposed a fusion mechanism for the distributed cooperative map matching (CMM) within the vehicular ad-hoc network. This mechanism makes the information from each node reachable within the network by other nodes without direct communication, thus improving the overall localization accuracy and robustness. Each node runs a Rao-Blackwellized particle filter (RBPF) that processes the Global Navigation Satellite System (GNSS) measurements of its own and its neighbors, followed by a map matching step that reduces or eliminates the GNSS atmospheric error. Then each node fuses its own filtered results with those from its neighbors for a better estimation. In this work, the complicated dynamics and fusion mechanics of these RBPFs are represented by a linear dynamical system. We proposed a distributed optimization framework that explores the model to improve both robustness and accuracy of the distributed CMM. The effectiveness of this distributed optimization framework is illustrated by simulation results on realistic vehicular networks drawn from data, compared with the centralized one and a decentralized one with random fusion weights.
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In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism makes the information from one group of connected vehicles interpenetrate to other groups without full communication between all nodes, thus improving the utility of information in a low connected vehicle penetration situation. We tested the approach using the dynamic traffic data collected in the Safety Pilot Model Deployment program in Ann Arbor Michigan, USA, with dynamic changing networks due to the traveling of vehicles and packet drops of the Dedicated Short-Range Communication. Results show enhancement of localization accuracy with errors reduced by up to 70 % even in complex dynamic scenarios.
This paper presents a cooperative vehicle sorting strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in the discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic Astar algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step. To improve the algorithm running time and address multiple shortest paths, a distributed stochastic Astar algorithm (DSA) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost). Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA method. The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA. In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system.
Emergent cooperative adaptive cruise control (CACC) strategies being proposed in the literature for platoon formation in the Connected Autonomous Vehicle (CAV) context mostly assume idealized fixed information flow topologies (IFTs) for the platoon, implying guaranteed vehicle-to-vehicle (V2V) communications for the IFT assumed. Since CACC strategies entail continuous information broadcasting, communication failures can occur in congested CAV traffic networks, leading to a platoons IFT varying dynamically. To enhance the performance of CACC strategies, this study proposes the idea of dynamically optimizing the IFT for CACC, labeled the CACC-OIFT strategy. Under CACC-OIFT, the vehicles in the platoon cooperatively determine in real-time which vehicles will dynamically deactivate or activate the send functionality of their V2V communication devices to generate IFTs that optimize the platoon performance in terms of string stability under the ambient traffic conditions. Given the adaptive Proportional-Derivative (PD) controller with a two-predecessor-following scheme, and the ambient traffic conditions and the platoon size just before the start of a time period, the IFT optimization model determines the optimal IFT that maximizes the expected string stability. The optimal IFT is deployed for that time period, and the adaptive PD controller continuously determines the car-following behaviors of the vehicles based on the unfolding degeneration scenario for each time instant within that period. The effectiveness of the proposed CACC-OIFT is validated through numerical experiments in NS-3 based on NGSIM field data. The results indicate that the proposed CACC-OIFT can significantly enhance the string stability of platoon control in an unreliable V2V communication context, outperforming CACCs with fixed IFTs or with passive adaptive schemes for IFT dynamics.
This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which mitigates the effect of losses. This objective is obtained by a switching controller that has a twofold objective: on the one hand, it promotes both plant stability and string stability of the average error dynamics by an $H_infty$ approach, and on the other hand it minimizes the variance around the average. We show by simulations that the proposed controller is able to compensate even for high probability of losses.
Vehicle-to-vehicle communications can be unreliable as interference causes communication failures. Thereby, the information flow topology for a platoon of Connected Autonomous Vehicles (CAVs) can vary dynamically. This limits existing Cooperative Adaptive Cruise Control (CACC) strategies as most of them assume a fixed information flow topology (IFT). To address this problem, we introduce a CACC design that considers a dynamic information flow topology (CACC-DIFT) for CAV platoons. An adaptive Proportional-Derivative (PD) controller under a two-predecessor-following IFT is proposed to reduce the negative effects when communication failures occur. The PD controller parameters are determined to ensure the string stability of the platoon. Further, the designed controller also factors the performance of individual vehicles. Hence, when communication failure occurs, the system will switch to a certain type of CACC instead of degenerating to adaptive cruise control, which improves the control performance considerably. The effectiveness of the proposed CACC-DIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the proposed CACC-DIFT design outperforms a CACC with a predetermined information flow topology.
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