Gm Cruise Holdings LLC

Amin Aghaei

Richard Stenson

Ashish Shrivastava

Ambrish Tyagi

Nivedita Chandrasekaran

Surya Dwarakanath

Ignacio Martin Bragado

Techniques for simulating LIDAR data are described. In one embodiment, a method for simulating LIDAR data may include retrieving a simulated scene that simulates a real-world scene, the simulated scene including at least one target object having a reflectivity r and located at a range R from a LIDAR sensor, the LIDAR sensor having at least one intrinsic parameter; generating a probability of detection (Pd) drop-off function for the LIDAR sensor, wherein the Pd drop-off function is related to r, R, and the at least one intrinsic parameter; for each data point including a ray emitted by the LIDAR sensor that hits the target object, generating a Pd value using the Pd drop-off function; and determining based on the Pd value whether to drop the data point.

Techniques for non-uniform LIDAR beam detection distance adjustment in an autonomous vehicle simulation environment

Systems and techniques are described for Light Detection and Ranging (LiDAR) noise modeling using machine learning (ML). An example method can include collecting, using one or more sensors, a first set of data for a simulation environment and generating, using the first set of data, a point cloud that represents and/or describes the simulation environment. The method can further include collecting, using the one or more sensors, a second set of data for a real-world environment and generating a noise model using the second set of data and a neural network. The method can also include generating, using the noise model and the point cloud, a noisy point cloud that represents and/or describes the real-world environment.

Noise modeling using machine learning

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Fremont, CA, United States of America

Amin Aghaei