arXiv:2605.22185v1 Announce Type: cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding. However, their application to safety-critical driving scenarios remains limited by an inability to accurately perceive and reason about rare high-stakes dynamic events, such as collisions or near-collisions. To address this, we introduce a pipeline that enhances MLLM perception by fusing downsampled video frames with synchronized high-frequency telematics data (IMU and GPS) and semantic insights from special
The rapid advancement of Multimodal Large Language Models (MLLMs) is now reaching a point where their limitations in safety-critical, real-world applications like autonomous driving are being directly addressed.
Improving MLLM perception in high-stakes dynamic events is crucial for the safe and widespread deployment of autonomous vehicles, impacting regulatory frameworks and public trust.
MLLMs are moving beyond general visual understanding to incorporate high-frequency sensor data and semantic insights, making them more robust for demanding real-time applications.
- · Autonomous vehicle developers
- · AI safety researchers
- · Sensor manufacturers
- · Companies relying solely on general-purpose MLLMs for critical applications
- · Human drivers (long-term decline)
Enhanced MLLMs will improve the reliability and safety metrics of autonomous driving systems.
Increased consumer confidence and regulatory approval will accelerate the adoption of self-driving cars.
The success in integrating MLLMs with sensor data in driving could set a precedent for other safety-critical AI applications, such as drone operation or robotic surgery.
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Read at arXiv cs.LG