Taillight Signal Recognition via Sequential Learning

Kotcharat Kitchat, Yen-Ju Chen, Min-Te Sun, Thattapon Surasak

Abstract: In autonomous driving, it is crucial to capture the driving intentions of other vehicles on the road, which can then be used for the autonomous driving vehicle to plan a safe route. This study proposes a system to identify the driving intention of other vehicles from their taillight signals. To achieve this goal, both the positions of taillights (i.e., spatial features) and the change of the status of taillights over time (i.e., temporal features) need to be properly extracted and recognized. In our system, a longer sequence of 32 frames is used as input to capture the complete change of taillights. In addition, a transfer-learned classical convolutional neural network and a light-weight WaveNet are adopted to extract spatial and temporal features of the input sequence, respectively. Moreover, the dataset is augmented to ensure the convergence of model training. The experiment results indicate that our system outperforms the state of the art approaches in taillight recognition.