A fine-tuned BLIP model with hierarchical question skipping achieves 423 ms average inference for driving scene description, with GPT-evaluated scores of 65 to 79, near GPT-4o's 77.
Reimagining an autonomous vehicle
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abstract
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development. Solving the technology will create social change which parallels the invention of the automobile itself. Today's autonomous driving technology is laudable, though rooted in decisions made a decade ago. We argue that a rethink is required, reconsidering the autonomous vehicle (AV) problem in the light of the body of knowledge that has been gained since the DARPA challenges which seeded the industry. What does AV2.0 look like? We present an alternative vision: a recipe for driving with machine learning, and grand challenges for research in driving.
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Hierarchical Question-Answering for Driving Scene Understanding Using Vision-Language Models
A fine-tuned BLIP model with hierarchical question skipping achieves 423 ms average inference for driving scene description, with GPT-evaluated scores of 65 to 79, near GPT-4o's 77.