Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Haritha Muralidharan

Kentaro Oguchi

Seyhan Ucar

Rui Guo

Emrah Akin Sisbot

Takamasa Higuchi

Xuewei Qi

Yashar Zeiynali Farid

Shalini Keshavamurthy

Xiaofei Cao

Wenjie Luo

Fangying Zhai

Systems and methods are provided for generating a real-time lane-level traffic map based on outbound sensor information. In particular, some embodiments generate lane-level traffic maps using radar sensor information. The populated real-time lane-level traffic maps are shared to other vehicles and may be used by autonomous vehicle systems to maneuver the vehicle.

System and method to generate a lane-level traffic map using vehicle sensor data

Systems, methods, and other embodiments described herein relate to estimating the origins of abnormal driving through observed driving patterns. In one embodiment, a method includes detecting, using parallel computations, abnormal classifications associated with a subject vehicle and nearby vehicles according to driving patterns derived from observation data, the abnormal classifications being associated with exceeding a position range in the observation data. The method also includes estimating an origin of abnormal driving with happens-before analysis according to the abnormal classifications, and the abnormal driving is associated with deviations in a traffic flow associated with the subject vehicle and the nearby vehicles. The method also includes controlling the subject vehicle using a driving command according to the origin.

Systems and methods for estimating the origins of abnormal driving

One embodiment of a system for analyzing the in-lane driving behavior of an external road agent generates a sequence of sparse 3D point clouds based on a sequence of depth maps corresponding to a sequence of images of a scene. The system performs flow clustering based on the sequence of depth maps and a sequence of flow maps to identify points across the sequence of sparse 3D point clouds that belong to a detected road agent. The system generates a dense 3D point cloud by combining at least some of those identified points. The system detects one or more lane markings and projects them into the dense 3D point cloud to generate an annotated 3D point cloud. The system analyzes the in-lane driving behavior of the detected road agent based, at least in part, on the annotated 3D point cloud.

Systems and methods for analyzing the in-lane driving behavior of a road agent external to a vehicle

A system for estimating depth using a monocular camera may include one or more processors, a monocular camera, and a memory device. The monocular camera and the memory device may be operably connected to the one or more processors. The memory device may include an image capture, an encoder-decoder module, a semantic information generating module, and a depth map generating module. The modules may configure the one or more processors to executed by one or more processors cause the one or more processors to obtain a captured image from the monocular camera, generate a synthesized image based on the captured image wherein the style transfer module was trained using a generative adversarial network, generate, a feature map based on the synthesized image, generate semantic information based on the feature map, and generate a depth map based on the feature map and the semantic information.

System and method for estimating depth using a monocular camera

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Santa Clara, CA, United States of America

Haritha Muralidharan