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Found 2 papers, 1 papers with code
Probing Multimodal LLMs as World Models for Driving
We provide a sober look at the application of Multimodal Large Language Models (MLLMs) within the domain of autonomous driving and challenge/verify some common assumptions, focusing on their ability to reason and interpret dynamic driving scenarios through sequences of images/frames in a closed-loop control environment.
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers.
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