  In-vehicle edge computing is a much anticipated paradigm to serve
ever-increasing computation demands originated from the ego vehicle, such as
passenger entertainments. In this paper, we explore the unique idea of
crowdsourcing passing-by vehicles to augment computing of the ego vehicle. The
challenges lie in the high dynamics of passing-by vehicles, time-correlated
task computation, and the stringent requirement of computing reliability for
individual user tasks. To this end, we formulate an optimization problem to
minimize the end-to-end latency by optimizing the task assignment and resource
allocation of user tasks. To address the complex problem, we propose a new
algorithm (named CAVE) with multiple key designs. We build an end-to-end
network and compute simulator and conduct extensive simulation to evaluate the
performance of the proposed algorithm.
