Privacy-Preserving Intersection Management for Autonomous Vehicles

Abstract

Traffic lights are a common instrument to regulate the traffic in junctions. However, when a vehicle has an urgency, it may violate the traffic lights. Since the other vehicles do not expect this, such violations lead to road accidents. Connected and autonomous vehicles can coordinate their actions and decide on the priority of passing without the need of traffic lights if they can share information about their current situation. That is, a vehicle with an urgency can communicate this with justifications to others and ask to go first. However, the shared information can potentially yield privacy violations while helping vehicles attain priority. We propose a privacy-preserving decision making framework for managing traffic at junctions. The vehicles are represented as autonomous agents that can communicate with each other and make priority-based decisions using auctions. The bids in the auctions are not monetary but contain information that each vehicle is willing to declare. Our experiments on realworld accident data show that our proposed bidding strategies help vehicles preserve their privacy while still enabling them to receive priority at junctions.