Action-State Joint Learning-Based Vehicle Taillight Recognition in Diverse Actual Traffic Scenes

As the vital factor of vehicle behavior understanding and prediction, vehicle taillight recognition is an important technology for autonomous driving, especially in diverse actual traffic scenes full of dynamic interactive traffic participants. However, in practical application, it always faces many challenges, such as 'variable lighting conditions', 'non-uniform taillight standards' and 'random relative observation pose', which lead to few mature solutions in current common autopilot systems. This work proposes an action-state joint learning-based vehicle taillight recognition method on the basis of vehicles detection and tracking, which takes both taillight state features and time series features into account, consequently getting practicable results even in complex actual scenes. In detail, vehicle tracking sequence is used as input and split into pieces through a sliding window. Then, a CNN-LSTM model is applied to simultaneously identify the action features of brake lights and turn signals, dividing taillight actions into five categories: None, Brake_on, Brake_off, Left_turn, Right_turn. Next, the brightness of high-position brake light is extracted through semantic segmentation and combined with taillight actions to form higher-level features for taillight state sequence analysis. Finally, an undirected graph model is used to establish the long-term dependence between successive pieces by analysing the higher-level features, thus inferring the continuous taillight state into: off, brake, left, right. Datasets including daytime, nighttime, congested road, highway, etc. were collected, tested and published in our work to demonstrate its effectiveness and practicability.