  Deep learning is the method of choice for trajectory prediction for
autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires
the availability of sufficiently rich and high-quality centralized datasets,
which easily leads to privacy leakage. Besides, uncertainty-awareness becomes
increasingly important for safety-crucial cyber physical systems whose
prediction module heavily relies on machine learning tools. In this paper, we
relax the data collection requirement and enhance uncertainty-awareness by
using Federated Learning on Connected Autonomous Vehicles with an
uncertainty-aware global objective. We name our algorithm as FLTP. We further
introduce ALFLTP which boosts FLTP via using active learning techniques in
adaptatively selecting participating clients. We consider both negative
log-likelihood (NLL) and aleatoric uncertainty (AU) as client selection
metrics. Experiments on Argoverse dataset show that FLTP significantly
outperforms the model trained on local data. In addition, ALFLTP-AU converges
faster in training regression loss and performs better in terms of NLL, minADE
and MR than FLTP in most rounds, and has more stable round-wise performance
than ALFLTP-NLL.
