Stanford Robotics Seminar ENGR319 | Winter 2026 | Bringing AI Up To Speed

Chess mastery versus driving failure

While AI surpassed human chess grandmasters years ago, self-driving cars still require human teleoperators and fail at mundane tasks because driving is an open system where "anything can happen" unlike chess's bounded rules.

The coverage complexity problem

Autonomous driving involves an impossibly vast hyperdimensional space spanning all possible objects, diverse environments (weather, culture, geometry), and temporal interactions that cannot be fully enumerated or trained.

Human generalization advantage

Humans effortlessly adapt to new vehicles and unfamiliar roads through generalized physical reasoning, while AVs freeze or fail in novel edge cases outside their Operational Design Domain.

Hallucinations in physical reasoning

Current generative AI creates cinematically realistic videos but fails at background physics—showing cyclists passing through vehicles or pedestrians spawning mid-road—revealing lack of causal understanding.

Virtual versus embodied intelligence

Language and vision AI operate in virtual environments, but physical AI requires understanding cause-and-effect, contact dynamics, and embodiment through real-world experience.

Non-negotiable safety requirements

Unlike chatbots where hallucinations are harmless, autonomous vehicles require zero-error physical reasoning because mistakes cause severe real-world consequences.

No consensus on safety metrics

The industry lacks a reliable measuring stick for AV safety because safety is contextual—what is safe in one environment may not transfer globally.

Comparative safety approaches

Researchers propose comparing systems via scenario embeddings rather than defining absolute safety, automatically mining datasets for comparable test cases without manual labeling.

Multi-modal scenario analysis

Recent work explores trajectory-space analysis and LLM-based reasoning to assess spati-temporal interactions, moving beyond pixel-level similarity to compare actual traffic behaviors.