GoKawiilDrivingBench test puts GPT-6 Astra, Grok 4.6, Claude Fable 5.1 in control of a real Toyota Corolla
Three computer scientists connected leading AI language models to a Toyota Corolla's steering, throttle and brakes using a comma four device and GPS telemetry, then had each model attempt a short parking-lot driving course with no human input beyond a foot hovering over the brake. Only OpenAI's GPT-6 Astra completed the full course, doing so on its second try in about five minutes over a distance of under 500 feet, while most other attempts failed to get past the first corner.
GoKawiil's interpretation of the reporting above, not reported fact.
The experiment suggests that even the most advanced general-purpose AI models still struggle with basic spatial perception tasks, like judging which side of a lane marking a car should be on, that are foundational to safe driving. This could indicate that general language-model intelligence does not straightforwardly translate into the specialized perception and control skills needed for autonomous vehicles, according to the researchers' own account of failure modes. It may also serve as a cautionary data point for those hoping frontier AI models could quickly generalize into physical-world control tasks.
- Only GPT-6 Astra completed the full DrivingBench parking-lot course, succeeding on its second attempt.
- Grok 4.6 and Claude Fable 5.1 largely failed to get past the first corner of the test course.
- Researchers attributed most failures to perception errors, such as misjudging lane boundaries marked by cones.
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Published there as: “Scientists Download Frontier AI Model Into Self-Driving Car Let It Loose”
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