We present theoretical results aimed at connecting the training methods of deep learning and GP models. We show that the Gaussian posterior approximations for Bayesian DNNs, such as those obtained by Laplace approximation and variational inference (VI), are equivalent to posterior distributions ofGPregression models.

A major challenge in robotics is to deploy simulated controllers into real-world.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Due to its ability to integrate information over large boxes, the new layer facilitates long-range propagation of information and leads to the efficient increase ofthe receptivefields ofnetwork units.

Mean A verage Precision (mAP). For VIC3D object detection, we focus on the obstacles around the ego vehicle. There are two metrics used for evaluation: BEV@mAP and 3D@mAP . BEV@mAP evaluates the 3D boxes in the bird's-eye view and ignores the In our implementation, we ignore the transmission cost of calibration files and timestamps. For early fusion, we calculate the transmission cost of transmitting raw data.

Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, the uncertain temporal asynchrony and limited communication conditions can lead to fusion misalignment and constrain the exploitation of infrastructure data.