Survey on Deep Multi-Task Learning Applications in Connected Autonomous Vehicles
A new survey paper on arXiv presents the first comprehensive review of deep multi-task learning (MTL) applied to connected autonomous vehicles (CAVs), covering perception, prediction, planning, control, and vehicle-to-everything (V2X) communications. CAVs traditionally rely on separate models for each task, creating high computational costs and real-time performance challenges that MTL aims to address within a unified framework. The survey identifies current research gaps and outlines future directions, potentially informing more efficient and scalable autonomous driving systems.
Published on arXiv and accepted to IEEE Communications Surveys & Tutorials, the survey by Jiayuan Wang and colleagues offers the first dedicated review of deep MTL methodologies in the context of CAVs. The authors organize their analysis across four core functional domains—perception, prediction, planning, and control—distinguishing between ego vehicle-only (onboard-only) approaches and V2X-enhanced cooperative multi-agent paradigms. A fifth domain covers V2X communications and radio resource management (RRM) as communication-centric MTL problems, reflecting the unique constraints of networked vehicle systems including latency, reliability, and bandwidth. The paper argues that MTL, by jointly learning multiple tasks within a single model, offers meaningful gains in computational efficiency and resource utilization compared to deploying separate specialized models. The authors conclude by cataloguing the strengths and limitations of existing methods, highlighting open research questions, and proposing directions for advancing MTL in real-world CAV deployments.
What's missing
The survey does not appear to include empirical benchmarking of the reviewed methods against one another, limiting direct performance comparisons. Open questions include how MTL models perform under adversarial conditions or sensor failures, and how regulatory and safety certification frameworks would accommodate unified multi-task models in production vehicles.
What different sources said
- arXiv cs.LGCenter
A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
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