YoloP-Based Pre-processing for Driving Scenario Detection

Recognition of driving scenarios is getting ever more relevant in research, especially for assessing performance of advanced driving assistance systems (ADAS) and automated driving functions. However, the complexity of traffic situations makes this task challenging. In order to improve the detection rate achieved through state-of-the-art deep learning models, we have investigated the use of the YoloP fully convolutional neural network architecture as a pre-processing step to extract high-level features for a residual 3D convolutional neural network We observed thar this approach reduces computational complexity, resulting in optimized model performance, also in terms of generalization from training on a synthetic dataset to testing in a real-world one.