Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Abstract

The development of autonomous vehicles is becoming increasingly popular and gathering real-world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy, it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state-of-the-art sensors and equipment installed onboard the UPCT’s CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain the speed and steering wheel angle and it obtained very promising results.

Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints

“…Estimating scene depth and camera pose from video sequences is a critical topic in visual perception and forms the foundation of many advanced tasks. Such estimations can be used to build 3D scene structures, which can be implemented in various industrial environments, including autonomous driving, visual navigation, and augmented reality [1][2][3]. Traditional methods rely on geometric cues in the image for inference, making them sensitive to challenging environments with low texture or strong lighting changes [4][5][6][7][8].…”

“…The main contributions of the work are twofold: (1) We introduce multiple mask techniques to mitigate the adverse impact of outliers in the scene during the view synthesis process. Additionally, we employ a MaskNet network to address the detrimental effects of outliers on pose estimation; (2) we propose several geometric consistency constraints to alleviate the limitations of sole training with photometric consistency. Finally, we evaluate our model on the widely used KITTI dataset and demonstrate its superiority over other unsupervised methods.…”