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Toyota Research Institute (TRI) and Stanford Engineering demonstrated two autonomous Toyota GR Supras drifting together on a closed track, with the chase car adjusting to follow the lead car without colliding. Stanford reported that the cars came within less than 10 inches of each other at times and reached speeds up to 35 mph. Those were conditions in a research demonstration—not specifications for a consumer self-driving car.
What the tandem-drifting demonstration involved
Announced on July 23, 2024, after nearly seven years of collaboration, the project paired two full-size Toyota GR Supras. The lead car followed a planned drifting path. The chase car responded to the lead car’s movement, aiming to stay close while avoiding a collision. Toyota and Stanford described the vehicles operating near the limits of control.
Drifting is a controlled maneuver in which a vehicle travels in a chosen direction while its tires have exceeded normal traction. Tandem drifting adds a second challenge: the following vehicle must account for another car’s changing position and trajectory while both are sliding. The headline’s “inches apart” describes moments during this demonstration, not a gap the cars maintained continuously.
How the cars planned their movements
Repeated trajectory and control planning
The system used nonlinear model predictive control (NMPC). It repeatedly planned vehicle trajectories and control inputs—steering, throttle, and braking—against objectives and physical or hardware constraints. The lead car’s objective was to sustain its planned drift; the chase car also had to follow the lead and avoid a collision. Toyota said the optimization was solved and re-solved up to 50 times per second. That figure describes the reported optimization rate, not a general specification for autonomous vehicles.
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Learning about tires and sharing information
Toyota described a neural-network tire model that learns from driving experience. The cars shared information over a dedicated Wi-Fi network, including their relative positions and intended trajectories. Stanford said GPS guided the vehicles. Because track conditions can change, the teams also described learning from successive trips to the track.
Why study a maneuver that looks so risky?
The point was to study vehicle control when tires are sliding at or beyond normal grip limits. Toyota and Stanford said the physics of drifting resemble some conditions a car may encounter on snow or ice. Better control near those limits could inform automated responses to a slide.
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Chris Gerdes, Stanford professor of mechanical engineering and co-director of the Center for Automotive Research, said the work had already led to “new techniques for controlling automated vehicles safely on ice.” That describes a potential safety application of the research; it does not mean the tandem-drifting system itself is deployed in consumer cars.
What this does—and does not—show about self-driving safety
The sequence demonstrates that researchers can coordinate two full-size cars through a demanding sliding maneuver in a controlled track setting. It does not establish that the system has been validated on public roads, that a production vehicle uses it, or that it can prevent crashes generally. The available accounts do not report an independently validated crash-reduction rate or a public-road safety outcome.
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A 2024 peer-reviewed paper offers related technical context: it describes NMPC control in the unstable sliding-tire regime and racetrack experiments with a modified Toyota GR Supra, including transitions between drifting and grip driving. It is related autonomous-drifting research, not proof that every method in the paper was identical to the tandem demonstration.
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Sources and technical context
- Toyota Research Institute and Stanford Engineering’s July 23, 2024 announcement describes the project, control approach, data-sharing and research motivation.
- Stanford University’s account reports the demonstration’s speed and closest separation.
- The 2024 peer-reviewed paper on autonomous drifting control describes related NMPC methods and racetrack experiments.
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