Autonomous driving is the task of driving a vehicle without human conduction.
( Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving )
In this paper, we introduce a sensitivity analysis approach for developing and evaluating a radar simulation, with the objective to identify the parameters with the greatest impact regarding the system under test.
Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data.
Geometric data acquired from real-world scenes, e. g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.
Deep reinforcement learning (DRL) is becoming a prevalent and powerful methodology to address the artificial intelligent problems.
The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.
While the latest automated vehicles (AVs) can handle most real-world scenarios they encounter, a major bottleneck for turning fully autonomous driving into reality is the lack of sufficient corner case data for training and testing AVs.
We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation.
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.).