From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models
arXiv:2512.05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances. Reliable temporal understanding is essential for such agents to anticipate events, attribute causes, and act safely in dynamic environments, yet this remains a significant challenge even for state-of-the-art (SoTA) VLMs. Prior video benchmarks have emphasized other content (sports, cooking, etc.), yet no existing benchmark f
The increasing deployment of Vision-Language Models (VLMs) in autonomous systems, particularly autonomous driving, necessitates robust temporal understanding for safety and reliability, a current frontier in VLM development.
Reliable temporal understanding in AI is crucial for autonomous agents to operate safely and effectively in dynamic, real-world environments, directly impacting the viability and public adoption of systems like autonomous driving.
This paper highlights a critical gap in current VLMs regarding temporal understanding for autonomous driving, suggesting a focus shift in AI research and benchmark development towards enabling safer, more context-aware autonomous systems.
- · AI researchers focusing on temporal reasoning
- · Autonomous driving companies integrating advanced VLMs
- · Manufacturers of ADAS (Advanced Driver-Assistance Systems)
- · Developers of robust AI perception systems
- · Autonomous driving companies with inadequate temporal AI capabilities
- · Benchmarks lacking practical, temporal autonomous driving scenarios
Improved safety and reliability of autonomous driving systems as VLMs gain better temporal understanding.
Accelerated deployment and public acceptance of autonomous vehicles due to enhanced predictive capabilities and reduced incidents.
The application of robust temporal understanding in VLMs extends beyond driving into other safety-critical autonomous agent domains, fostering broader AI-powered automation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI