LightFormer: An End-to-End Model for Intersection Right-of-Way
  Recognition Using Traffic Light Signals and an Attention Mechanism
Zhenxing Ming Julie Stephany Berrio Mao Shan Eduardo Nebot Stewart Worrall
For smart vehicles driving through signalised intersections, it is crucial to
determine whether the vehicle has right of way given the state of the traffic
lights. To address this issue, camera based sensors can be used to determine
whether the vehicle has permission to proceed straight, turn left or turn
right. This paper proposes a novel end to end intersection right of way
recognition model called LightFormer to generate right of way status for
available driving directions in complex urban intersections. The model includes
a spatial temporal inner structure with an attention mechanism, which
incorporates features from past image to contribute to the classification of
the current frame right of way status. In addition, a modified, multi weight
arcface loss is introduced to enhance the model classification performance.
Finally, the proposed LightFormer is trained and tested on two public traffic
light datasets with manually augmented labels to demonstrate its effectiveness.