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Autonomous driving will become pervasive in the coming decades. iDrivingimproves the safety of autonomous driving at intersections and increasesefficiency by improving traffic throughput at intersections. In iDriving,roadside infrastructure remotely drives an autonomous vehicle at anintersection by offloading perception and planning from the vehicle to roadsideinfrastructure. To achieve this, iDriving must be able to process voluminoussensor data at full frame rate with a tail latency of less than 100 ms, withoutsacrificing accuracy. We describe algorithms and optimizations that enable itto achieve this goal using an accurate and lightweight perception componentthat reasons on composite views derived from overlapping sensors, and a plannerthat jointly plans trajectories for multiple vehicles. In our evaluations,iDriving always ensures safe passage of vehicles, while autonomous driving canonly do so 27% of the time. iDriving also results in 5x lower wait times thanother approaches because it enables traffic-light free intersections.

iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving