Gm Global Technolgoy Operations, Inc.

Embark Trucks Inc.

Mark Liu

Roya Sabbagh Novin

Elliot Branson

Gabriel Warshauer-Baker

Siddhartho Bhattacharya

Haggai Megged Nuchi

Provided is a system and method that can control a speed of an autonomous vehicle when approaching a vehicle on a shoulder based on a piecewise linear function. Further, the system and method may determine whether to perform a lane change based on various factors. In one example, the method may include storing a piecewise linear function, detecting a vehicle on a shoulder (VOS) of a road based on sensor data sensed by one or more sensors of an ego vehicle approaching the VOS, determining that the VOS is within a predefined distance of the ego vehicle, and in response to the determination, controlling the ego vehicle to slow down and then speed up based on the piecewise linear function.

Lane change determination for vehicle on shoulder

An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Autonomous vehicle that is controlled based upon output of a Bayesian object classifier system

Systems and method are provided for identifying free space between objects. The method includes retrieving, by a processor, a voxel grid around the vehicle wherein the voxel grid has a plurality of columns. The method further includes tracing, by the processor, lidar beams from a lidar system on the vehicle through the voxel grid, identifying, by the processor, a max subarray for each column in the voxel grid, classifying, by the processor, each column with a free space level, and outputting, by the processor, the classified columns as an image.

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San Francisco, CA, United States of America

Mark Liu