公开(公告)号：WO2022192266A2

公开(公告)日：2022-09-15

申请号：PCT/US2022/019354

申请日：2022-03-08

Applicant: RIDECELL, INC.

Inventor： KUMAR, Arun, Kumar Chockalingam Santha ,  SINGH, Paridhi ,  SINGH, Gaurav

IPC: G06T7/11 ,  B60W2420/52 ,  B60W2554/4029 ,  B60W2554/4041 ,  B60W60/0015 ,  G01S17/42 ,  G01S17/89 ,  G01S17/931 ,  G06N5/027 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30252 ,  G06T2207/30261 ,  G06T7/521 ,  G06T7/73 ,  G06T7/74

Abstract: Multi-object tracking in autonomous vehicles uses both camera data and LiDAR data for training, but not LiDAR data at query time. Thus, no LiDAR sensor is on a piloted autonomous vehicle. Example systems and methods rely on camera 2D object detections alone, rather than 3D annotations. Example systems/methods utilize a single network that is given a camera image as input and can learn both object detection and dense depth in a multimodal regression setting, where the ground truth LiDAR data is used only at training time to compute depth regression loss. The network uses the camera image alone as input at test time (i.e., when deployed for piloting an autonomous vehicle) and can predict both object detections and dense depth of the scene. LiDAR is only used for data acquisition and is not required for drawing 3D annotations or for piloting the vehicle.

公开(公告)号：WO2022119947A1

公开(公告)日：2022-06-09

申请号：PCT/US2021/061442

申请日：2021-12-01

Applicant: RIDECELL, INC.

Inventor： PANDYA, Samyak ,  CHOWDHURY, Jit Ray ,  REDDY, Srinivas, Aellala ,  GUPTA, Nalin ,  CHAKRABORTY, Arpan ,  SINGH, Gaurav

IPC: H04W4/40 ,  H04W4/44 ,  H04W4/46 ,  G01S13/931 ,  G01S15/931 ,  G01S17/931 ,  G06Q40/08

Abstract: An example method for extracting traffic scenarios from vehicle sensor data is disclosed. The example method includes acquiring vehicle data generated by one or more sensors coupled to a vehicle. The vehicle data is at least partially indicative of the surroundings of the vehicle during a particular time frame. The vehicle data is analyzed to identify objects in the surroundings of the vehicle and to determine the motion of the vehicle relative to the surroundings during the particular time frame. A plurality of events are defined, each indicative of a relationship between the vehicle and the objects. A scenario is defined as a particular combination of the events. Portions of the vehicle data in which the combination of elements occurs during a time interval are identified, and at least some of the identified data is extracted to a predefined data structure to create an extracted scenario.

公开(公告)号：WO2023014998A1

公开(公告)日：2023-02-09

申请号：PCT/US2022/039619

申请日：2022-08-05

Applicant: RIDECELL, INC.

Inventor： KUMAR, Arun Kumar, Chockalingam Santha ,  SINGH, Paridhi ,  SINGH, Gaurav

IPC: G06T7/593 ,  G01S17/89 ,  G06T7/50 ,  H04N13/243

Abstract: A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.