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Survey details deep multi-task learning for autonomous vehicles

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive review of deep multi-task learning (MTL) techniques applied to connected autonomous vehicles (CAVs). It explores how MTL can enable a single model to handle diverse tasks like perception, prediction, planning, and control, which is crucial for efficient and real-time operation in complex driving scenarios. The survey categorizes existing research based on whether tasks are performed solely by the ego vehicle or enhanced through vehicle-to-everything (V2X) communication, and also examines MTL in the context of V2X communications and radio resource management. The authors identify current research gaps and suggest future directions for advancing MTL in CAV systems. AI

IMPACT Provides a structured overview of multi-task learning applications for autonomous driving systems.

RANK_REASON Academic survey paper on a specific AI technique applied to a domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details deep multi-task learning for autonomous vehicles

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Academic survey paper on a specific AI technique applied to a domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang ·

    A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

    arXiv:2508.00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments. Moreover, through vehi…

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