  The emerging vehicular connected applications, such as cooperative automated
driving and intersection collision warning, show great potentials to improve
the driving safety, where vehicles can share the data collected by a variety of
on-board sensors with surrounding vehicles and roadside infrastructures.
Transmitting and processing this huge amount of sensory data introduces new
challenges for automotive edge computing with traditional wireless
communication networks. In this work, we address the problem of traditional
asymmetrical network resource allocation for uplink and downlink connections
that can significantly degrade the performance of vehicular connected
applications. An end-to-end automotive edge networking system, FAIR, is
proposed to provide fast, scalable, and impartial connected services for
intelligent vehicles with edge computing, which can be applied to any traffic
scenes and road topology. The core of FAIR is our proposed symmetrical network
resource allocation algorithm deployed at edge servers and service adaptation
algorithm equipped on intelligent vehicles. Extensive simulations are conducted
to validate our proposed FAIR by leveraging real-world traffic dataset.
Simulation results demonstrate that FAIR outperforms existing solutions in a
variety of traffic scenes and road topology.
