  With the rapid development of autonomous driving technologies, it becomes
difficult to reconcile the conflict between ever-increasing demands for high
process rate in the intelligent automotive tasks and resource-constrained
on-board processors. Fortunately, vehicular edge computing (VEC) has been
proposed to meet the pressing resource demands. Due to the delay-sensitive
traits of automotive tasks, only a heterogeneous vehicular network with
multiple access technologies may be able to handle these demanding challenges.
In this paper, we propose an intelligent task offloading framework in
heterogeneous vehicular networks with three Vehicle-to-Everything (V2X)
communication technologies, namely Dedicated Short Range Communication (DSRC),
cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave)
communication. Based on stochastic network calculus, this paper firstly derives
the delay upper bound of different offloading technologies with a certain
failure probability. Moreover, we propose a federated Q-learning method that
optimally utilizes the available resources to minimize the
communication/computing budgets and the offloading failure probabilities.
Simulation results indicate that our proposed algorithm can significantly
outperform the existing algorithms in terms of offloading failure probability
and resource cost.
