Autonomous vehicles require object detection systems to navigate traffic and avoid obstacles on the road. However, current detection methods often suffer from diminished detection capabilities due ...

Bolstering the safety of self-driving cars with a deep learning-based object detection system. ScienceDaily . Retrieved May 13, 2025 from www.sciencedaily.com / releases / 2022 / 12 / 221212140800.htm ...

3D object detection (3DOD) is central to real-world vision systems and a critical component in the development of perception capabilities for autonomous vehicles (AVs) and mobile autonomous robots.

Instead, Zhou and Tuzel propose the implementation of a trainable deep architecture for point cloud based 3D detection. The framework, called VoxelNet, uses voxel feature encoding (VFE) layers to ...

Synopsis: Ritsumeikan University researchers introduce DPPFA−Net, a groundbreaking 3D object detection network melding LiDAR and image data to improve accuracy for robots and self-driving cars.

Unsupervised learning eliminates the need for human input in creation of the AI engine. It uses unlabeled data and derives the underlying semantics and patterns which are then used to make decisions.

Three-dimensional object detection is crucial for autonomous vehicles. It utilizes point cloud data generated by LiDAR to ...