Semi-Automatic High-Accuracy Labelling Tool for Multi-Modal Long-Range Sensor Dataset

Many research works have contributed to achieve SAE levels 3 and 4 in some pre-defined areas under certain restrictions. A deeper scene understanding and precise predictions of drivers intentions are needed to continue improving autonomous driving capabilities to reach higher SAE levels. Deployment of accurate and detailed datasets could be considered as one of the most pressing needs to enhance autonomous driving capabilities. This work presents a novel data acquisition methodology for on-road vehicle trajectory collection. The proposed sensor setup improves the range and detection accuracy by using a high accuracy laser scanner covering 360° and two high-speed and high-resolution cameras. The sensor fusion increases the labelling resolution and extends the detection range sporting the best of each sensor. A Median Flow tracking algorithm and a Convolutional Neural Network enable a semi-automatic labelling process, which reduces the effort to create detailed annotated datasets. High accurate trajectories are reconstructed with few manual annotations up to 60m with a mean error below 2 cm. This methodology has been developed with a view to creating a dataset which enables the development of advanced vehicle trajectory prediction systems, and thus to contribute to human-like automated driving.