Bringing simulation to life: A mixed reality autonomous intersection

Fully autonomous vehicles are technologically feasible with the current generation of hardware, as demonstrated by recent robot car competitions. Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that with autonomous vehicles it is possible to make intersection control much more efficient than the traditional control mechanisms such as traffic signals and stop signs. The protocol, however, has only been tested in simulation and has not been evaluated with real autonomous vehicles. To realistically test the protocol, we implemented a mixed reality platform on which an autonomous vehicle can interact with multiple virtual vehicles in a simulation at a real intersection in real time. From this platform we validated realistic parameters for our autonomous vehicle to safely traverse an intersection in AIM. We present several techniques to improve efficiency and show that the AIM protocol can still outperform traffic signals and stop signs even if the cars are not as precisely controllable as has been assumed in previous studies.

Authors

• Michael J. Quinlan
• Tsz-Chiu Au
• Jesse Zhu
• Nicolae Stiurca
• Peter Stone 0001

Keywords

• Vehicles
• Acceleration
• Mobile robots
• Throughput
• Delay
• Driver circuits
• Trajectory
• Mixed Reality
• Autonomous Intersection
• Light Signal
• Autonomous Vehicles
• Real Vehicle
• Stop Sign
• Traffic Stops
• Optimization Procedure
• Arrival Time
• Traffic Congestion
• Speed Limit
• Average Delay
• Multi-objective Optimization Problem
• Network Throughput
• Arrival Rate
• Buffer Size
• Maximum Acceleration
• Queueing System
• Vehicle Acceleration
• Large Buffer
• Feasible Schedule
• Max Speed
• Traffic Levels
• Reserve System