  This letter studies an ultra-reliable low latency communication problem
focusing on a vehicular edge computing network in which vehicles either fetch
and synthesize images recorded by surveillance cameras or acquire the
synthesized image from an edge computing server. The notion of risk-sensitive
in financial mathematics is leveraged to define a reliability measure, and the
studied problem is formulated as a risk minimization problem for each vehicle's
end-to-end (E2E) task fetching and offloading delays. Specifically, by
resorting to a joint utility and policy estimation-based learning algorithm, a
distributed risk-sensitive solution for task fetching and offloading is
proposed. Simulation results show that our proposed solution achieves
performance improvements up to 40% variance reduction and steeper distribution
tail of the E2E delay over an averaged-based baseline.
