Zoox, Inc.

Muhammad Farooq Rama

Andrew Scott Crego

Kai Zhenyu Wang

Siavosh Rezvan Behbahani

Gowtham Garimella

Mahsa Ghafarianzadeh

Gerrit Bagschik

Rasmus Fonseca

Aditya Pramod Khadilkar

Techniques for modeling the probability distribution of errors in perception systems are discussed herein. For example, techniques may include modeling error distribution for attributes such as position, size, pose, and velocity of objects detected in an environment, and training a mixture model to output specific error probability distributions based on input features such as object classification, distance to the object, and occlusion. The output of the trained model may be used to control the operation of a vehicle in an environment, generate simulations, perform collision probability analyses, and to mine log data to detect collision risks.

Perception error modeling

Techniques associated with generating simulation scenarios for simulating a vehicle controller are discussed herein. Log data may include sensor data captured by sensors of a vehicle. The log data may represent objects in an environment. Objects may be associated with a region of a discretized representation of the environment relative to the vehicle. Specific states of objects (relative position in a region type, velocity, classification, size, etc.) may represent an instance of an occupation. Log data can be aggregated based on similar region type and/or object state. A statistical model over object states can be determined for each region type and can later be sampled to determine simulation parameters. A simulation scenario can be generated based on the simulation parameters, and a vehicle controller can be evaluated based on the simulation scenario.

System for generating generalized simulation scenarios

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Santa Clara, CA, United States of America

Muhammad Farooq Rama