  Optical camera communications (OCC) has emerged as a key enabling technology
for the seamless operation of future autonomous vehicles. In this paper, we
introduce a spectral efficiency optimization approach in vehicular OCC.
Specifically, we aim at optimally adapting the modulation order and the
relative speed while respecting bit error rate and latency constraints. As the
optimization problem is NP-hard problem, we model the optimization problem as a
Markov decision process (MDP) to enable the use of solutions that can be
applied online. We then relaxed the constrained problem by employing Lagrange
relaxation approach before solving it by multi-agent deep reinforcement
learning (DRL). We verify the performance of our proposed scheme through
extensive simulations and compare it with various variants of our approach and
a random method. The evaluation shows that our system achieves significantly
higher sum spectral efficiency compared to schemes under comparison.
