Survey details deep multi-task learning for autonomous vehicles

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 →

• Connected Autonomous Vehicles
• Jiayuan Wang
• Vehicle-to-Everything

• paper
• other

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