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The safety impacts of cooperative platooning in mixed traffic consisting of human-driven, con-nected, and connected-automated vehicles were evaluated. The cooperative platooning in mixed traffic control algorithm evaluated is the Cooperative Adaptive Cruise Control with unconnected Vehicle (CACCu) with an unconnected vehicle. Its safety and string stability were evaluated using a high-fidelity simulation based on real-world vehicle trajectories. An Adaptive Cruise Control (ACC) algorithm was selected for comparison purposes. The results indicate that the cooperative platooning in mixed traffic control algorithm (CACCu) maintains string stability and performs more safely than the ACC.
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Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control
Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.
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- Award ID(s):
- 1646019
- PAR ID:
- 10313399
- Date Published:
- Journal Name:
- Journal of Dynamic Systems, Measurement, and Control
- Volume:
- 144
- Issue:
- 1
- ISSN:
- 0022-0434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4052272
