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Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme’s efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.
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Vehicle-to-Everything Communication Using a Roadside Unit for Over-the-Horizon Object Awareness
Self-driving and automated vehicles rely on a comprehensive understanding of their surroundings and one another to operate effectively. While the use of sensors may allow the vehicles to directly perceive their environments, there are instances where information remains hidden from a vehicle. To address this, vehicles can transmit information between each other, enabling over-the-horizon awareness. We create a Robot Operating System simulation of vehicle-to-everything communication. Then, using two real-life electric vehicles equipped with global positioning systems and cameras, we aggregate time, position, and navigation information into a central database on a roadside unit. Our model uses an image classification deep learning model to detect obstacles on the road. Next, we create a web-based graphical user interface that automatically updates to display the vehicles and obstacles from the database. Finally, we use an occupancy grid to predict vehicle trajectories and prevent potential collisions. Our deep learning model has a precision-recall score of 0.995 and our system works across many devices. In the future, we aim to recognize a broader range of objects, including pedestrians, and use multiple roadside units to widen the scope of the model.
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- PAR ID:
- 10576887
- Publisher / Repository:
- IEOM Society International
- Date Published:
- ISBN:
- 979-8-3507-0550-8
- Format(s):
- Medium: X
- Location:
- Detroit, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.46254/EV01.20230202
