  Expected to provide higher transportation efficiency and security, autonomous
driving has attracted substantial attentions from both industry and academia.
Meanwhile, the emergence of edge intelligence has further introduced
significant advancements to this field. However, the crucial demands of
ultra-reliable and low-latency communications (URLLC) among the vehicles and
edge servers have hindered the development of autonomous driving. In this
article, we provide a brief overview of edge intelligence enabled autonomous
driving system and current vehicle-to-everything (V2X) technologies. Moreover,
challenges associated with massive data transmission in autonomous driving are
highlighted from three perspectives: multi-modal data transmission and fusion,
multi-user collaboration and connection, and multi-task training and execution.
To cope with these challenges, we propose to incorporate semantic communication
into autonomous driving to achieve highly efficient and task-oriented data
transmission. Unlike traditional communications, semantic communication
extracts task-relevant semantic feature from multi-sensory data. Specifically,
a unified multi-user semantic communication system for transmitting multi-modal
data and performing multi-task execution is designed for collaborative data
transmission and decision making in autonomous driving. Simulation results
demonstrate that the proposed system can significantly reduce data transmission
volume without compromising task performance, as evidenced by the realization
of a cooperative multi-vehicle target classification and detection task.
