Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and
  Bayesian Inference
M. Manzour A. Ballardini R. Izquierdo M. A. Sotelo
Prediction of vehicle lane change maneuvers has gained a lot of momentum in
the last few years. Some recent works focus on predicting a vehicle's intention
by predicting its trajectory first. This is not enough, as it ignores the
context of the scene and the state of the surrounding vehicles (as they might
be risky to the target vehicle). Other works assessed the risk made by the
surrounding vehicles only by considering their existence around the target
vehicle, or by considering the distance and relative velocities between them
and the target vehicle as two separate numerical features. In this work, we
propose a solution that leverages Knowledge Graphs (KGs) to anticipate lane
changes based on linguistic contextual information in a way that goes well
beyond the capabilities of current perception systems. Our solution takes the
Time To Collision (TTC) with surrounding vehicles as input to assess the risk
on the target vehicle. Moreover, our KG is trained on the HighD dataset using
the TransE model to obtain the Knowledge Graph Embeddings (KGE). Then, we apply
Bayesian inference on top of the KG using the embeddings learned during
training. Finally, the model can predict lane changes two seconds ahead with
97.95% f1-score, which surpassed the state of the art, and three seconds before
changing lanes with 93.60% f1-score.