From Perception to Computation: Revisiting Delay Optimization for Connected Autonomous Vehicles

Abstract

With the development of sensing, wireless communication, and real-time computing technologies, vehicles are gradually becoming more and more intelligent. To provide safe autonomous mobility services, connected autonomous vehicles (CAVs) need to obtain complete information about their environment and process it in real-time to make driving decisions. However, the rapid increase in data volume puts pressure on CAVs to process tasks in real time. This survey analyzes CAVs delay optimization from the perception layer, communication layer, computation layer, and cross-layer. According to different coordination modes, each layer of CAVs is divided, and the problem of delay optimization is classified in fine granularity. This survey will help researchers gain insight into the mechanism of delay optimization on CAVs and highlight the key role of optimized delay in autonomous driving.