Survey on Deep Multi-Task Learning Applications in Connected Autonomous Vehicles
Researchers have published a comprehensive survey examining how deep multi-task learning (MTL) can be applied to connected autonomous vehicles (CAVs) that must perform perception, prediction, planning, and control simultaneously. Multi-task learning allows these vehicles to handle multiple functions within a single unified model rather than separate models, improving efficiency and reducing computational overhead. This approach is particularly important as CAVs increasingly rely on vehicle-to-everything (V2X) communication for cooperative driving while operating under strict latency, reliability, and bandwidth constraints.
A new survey paper on arXiv reviews deep multi-task learning approaches for connected autonomous vehicles, covering functional domains including perception, prediction, planning, control, V2X communications, and radio resource management. The survey distinguishes between ego vehicle-only systems (using onboard sensors and computation) and V2X-enhanced cooperative systems (multi-agent approaches using vehicle-to-everything communication). Rather than deploying separate specialized models for each driving task, multi-task learning enables joint learning within a unified model, addressing key challenges in autonomous vehicle deployment such as high computational costs, real-time performance requirements, and resource constraints. The authors identify this as the first comprehensive review specifically focused on deep MTL in CAVs and discuss both the strengths and limitations of existing methods while proposing future research directions.
What different sources said
- arXiv cs.LGCenter
A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
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