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In a reported low-speed experiment called DrivingBench, GPT-6 Astra completed a 134.7-meter cone course in a real Toyota Corolla on its second attempt. Grok 4.6 made it as far as 11% of the course. That is a striking demonstration of models controlling a vehicle through an experimental interface—not evidence that ChatGPT can safely drive on public roads or that Grok literally crashed immediately.
What happened in the DrivingBench test?
As described by Auto Connected Car News on October 5, 2026, researchers gave four general-purpose AI models a way to observe and control a real car on a low-speed parking-lot course. The test used a 2022 Toyota Corolla fitted with a Comma Four aftermarket device running modified openpilot. A custom software harness supplied camera frames and vehicle state, and let a model send steering and speed commands for a specified duration or stop the car.
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The reported speed limit was 3.5 meters per second, about 8 mph. Commands replaced the active command rather than waiting in a queue, and the setup included additional safety limits. A human operator remained behind the wheel, ready to brake. The course measured 134.7 meters and was marked with cones.
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The reported percentages represent course progress, not success rates across a large number of independent trials. Each model had up to three attempts in one continuing conversation; after a failed attempt, the operator asked it to reflect on what went wrong. The retries therefore shared context.
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| Model | Reported course progress | Completion and other reported details |
|---|---|---|
| GPT-6 Astra | About 49% on the first attempt | Completed on the second attempt; successful run took 5 minutes and 22 seconds |
| Claude Fable 5.1 | 9%, 10%, then 45% | Did not complete the course |
| Grok 4.6 | 8%, 11%, then 10% | Best reported progress was 11%; did not complete |
| GPT-5.6 Sol | 6% on each of three attempts | Did not complete the course |
The detailed results and setup are reported by Auto Connected Car News. The headline phrase “crashed immediately” compresses the scorecard: Grok’s attempts reached 8%, 11%, and 10%, rather than ending at zero progress. The account does not establish a general ranking of the models.
What does the successful run reveal—and what does it not?
Astra’s completion was cautious and slow. The report says it averaged below 1 meter per second, never exceeded 0.8 meters per second, and used full steering lock on 20 of its 24 commands. It also reports that Astra switched to shorter, slower movements after reflecting on its first run. Claude improved its measured progress across attempts but still did not finish.
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Those details matter because completing a simple route is not the same as demonstrating robust driving. The reported failures included misreading which side of a diagonal cone boundary marked the lane, misjudging the car’s width from camera images, and deliberation delays while the vehicle remained in motion. The result shows that a model could make progress and, in this particular setup, finish a constrained course. It does not show reliable perception, safe responses to unpredictable road users, or readiness for public roads.
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The evaluation was small and tightly bounded. Each model was evaluated once, with its three attempts connected by a shared conversation rather than run as independent trials. The course was a controlled cone route, the speed was capped, and a human could intervene. The reported setup also had camera blind spots near the car and to its sides and rear, plus steering constraints from the Comma system. The account notes a flaw in an example used to explain steering.
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- Few observations: One session per model cannot establish how consistently a model would perform across different routes, conditions, or repeated independent runs.
- Limited environment: A parking-lot cone course does not test the full range of traffic, weather, pedestrians, signs, road rules, and unexpected events encountered on public roads.
- Restricted view and controls: Camera blind spots and the interface’s steering limits affect what the models could perceive and do.
- Human supervision: An operator ready to brake is an important safety backstop; the demonstration did not test unattended operation.
The detailed account refers to a paper submitted September 30, 2026, and a public benchmark, but the original paper, code, and telemetry are not established here. The reported figures should therefore be read as results from the described experiment, not as independently reproduced measures or a population-level safety statistic.
How does this relate to the NHTSA evaluation?
The same story reports that the National Highway Traffic Safety Administration opened preliminary evaluation PE26007 on September 21, 2026, concerning Comma Three, Comma 3X, Comma Four, and openpilot after reports of crashes involving equipped vehicles. The account says the evaluation does not itself establish that the hardware or software caused those crashes. This is separate context from DrivingBench: the reported road incidents and the low-speed parking-lot demonstration are not the same test or evidence. The agency record was not independently verified for this article, so consult the agency’s materials for the official status and findings.
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What should drivers take away?
DrivingBench is a meaningful proof of capability: general-purpose models were connected to controls for a real car and attempted a defined course, and one completed it under slow, supervised conditions. But the experiment does not validate connecting a chatbot to a consumer vehicle, establish a safety rate, or show that a model can replace a dedicated autonomous-driving system. The measured outcome is narrower: in this specific test, Astra finished on its second try, while Grok’s best reported progress was 11%.
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