Gm Cruise Holdings LLC

Gm Global Technolgoy Operations, Inc.

Samuel Abrahams

Robert Tsai

Pradeep Bhatta

Rostyslav Lesiv

Zhe Zhang

Tommaso Colombo

Bradley Larkin Saund

Batyr Nurbekov

Sidney Oderberg

Christopher Kniffin

Dmitry Panin

Cody Neil

Rahul Gupta

Ujjwal Das Gupta

Sagar Tatipamula

Disclosed are embodiments for facilitating purposeful stress testing of autonomous vehicle response time with simulation. In some aspects, an embodiment includes receiving a request to launch a simulation scenario on an autonomous vehicle (AV) that is to operate on a real-world test course; initiating a simulation derived from the simulation scenario using a simulation driver that is executing on the AV; engaging operation of the AV on the real-world test course; coordinating a simulated AV position in the simulation with a physical AV position of the AV on the real-world test course; combining virtual entities from the simulation with physical entities on the real-world test course into a list of tracked objects for the AV; and causing the AV to respond to the list of tracked objects including the virtual entities and the physical entities during the operation of the AV on the real-world test course.

Purposeful stress testing of autonomous vehicle response time with simulation

Systems and methods are provided herein for an AV to more efficiently, quickly, and accurately determine a likelihood of collision with another object, for example, by recursive triangulation. A candidate trajectory of the AV is then selected for controlling the AV based on the determined likelihood(s) of collision. According to one example, the likelihood of collision is determined by: 1) recursively triangulating a convex hull that defines all of the locations in which a collision would occur between the object and the AV (e.g., in a manner similar to the Sierpiński method), and 2) multiplying the area of each resulting triangular region by the value of a probability density that defines all of the locations in which a collision would occur between the object and the AV function in that region.

Fast probability-of-collision computation

Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

Computing system for assigning maneuver labels to autonomous vehicle sensor data

Described herein are various technologies that pertain to controlling an AV based upon a neural-network-generated prediction of a trajectory (e.g., one or more future positions) of an object in the driving environment of the AV. With more specificity, described herein are various technologies pertaining to generating predictions of a trajectory of an object by way of a multi-headed recurrent neural network (RNN). The multi-headed RNN is configured to output probability data that indicates a probability of an object moving to a position at a future point in time when the object has each of a plurality of different object characteristics. The multi-headed RNN is further configured to use state information of the multi-headed RNN from prior predictions of a trajectory of an object in connection with generating a future prediction of a trajectory of the object.

Multi-headed recurrent neural network (RNN) for multi-class trajectory predictions

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San Francisco, CA, United States of America

Samuel Abrahams