Other

Toyota Jidosha Kabushiki Kaisha

Toyota Motor Engineering & Manufacturing North America, Inc.

Denso Corporation

Emrah Akin Sisbot

Kentaro Oguchi

Seyhan Ucar

Yusuke Nakano

Eric Martinson

Yongkang Liu

Akansel Cosgun

Veera Ganesh Yalla

Takamasa Higuchi

Takuya Hasegawa

Veeraganesh Yalla

Hirokazu Nomoto

Yutaka Takaoka

Haritha Muralidharan

Halit Bener Suay

Tomohiro Matsuda

Yashar Zeiynali Farid

Xuewei Qi

Rahul Parundekar

David Kim

John Mark Agosta

Joseph Djugash

Young-Ho Kim

Xiaofei Cao

A method for detecting objects that are likely to fall from vehicle cargo includes obtaining first data of a cargo of a first vehicle captured at a first time and second data of the cargo captured by a second vehicle in an environment of the first vehicle at a second time after the first time, determining a first distance between two key points of the first data or a first shape constructed by key points of the first data and a second distance between two key points of the second data or a second shape constructed by key points of the second data, and detecting a movement of the cargo based on a comparison of the first distance and the second distance or a comparison of the first shape and the second shape.

Methods and systems for a detection of objects that are likely to fall from vehicle cargo

Systems and methods are provided for programmatically determining traffic management operations in response to conflicts on a roadway. For example, the systems and methods may determine a first traffic management instruction for a first vehicle for a first conflict on a roadway; access characteristics of the first vehicle associated with the first management instruction in response to the first conflict; determine a second conflict occurring after and connected to the first conflict; using the characteristics of the first vehicle, determine a second traffic management instruction for the first vehicle for the second conflict; and provide the second traffic management instruction to the first vehicle.

Traffic management after an initial conflict event

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

A method according to some embodiments includes sensing, by a sensor set of the ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method further includes classifying a type of the remote vehicle based on the sensor data. The method further includes retrieving a behavior profile for the type of the remote vehicle that identifies criteria for the type of the remote vehicle and classifications of abnormal behavior and normal behavior based on the criteria. The method further includes comparing the behavior of the remote vehicle to the behavior profile for the type of the remote vehicle. The method further includes determining that the behavior of the remote vehicle is normal based on the comparing.

Detection of abnormal driving based on behavior profiles

Systems and methods are provided for detecting when a vehicle engages in anomalous driving behavior and managing nearby vehicles to mitigate risk to the nearby vehicles due to the anomalous driving behavior. According to some embodiments, the methods and systems comprise receiving an indication that an anomalous driving behavior of a first vehicle is detected, and identifying a plurality of vehicles within a geographic area of the first vehicle based on receiving the indication. The methods and systems further include generating an arrangement for the plurality of vehicles based on the detected anomalous driving behavior, determining a target speed for each of the plurality of vehicles based on the determined arrangement and a current speed of each of the plurality of vehicles, and communicating control messages to each of the plurality of vehicles. The control messages comprising the target speed to distribute the plurality of vehicles into the determined arrangement.

Systems and methods to manage drivers under abnormal driving

Systems and methods are provided for estimating the energy range of an electric vehicle, including updating an energy consumption estimation model based on the location of a vehicle or based on the current time. One embodiment comprises obtaining a route of a vehicle and deploying an energy consumption estimation model to provide an energy consumption estimate along the route from a first location to a destination. In response to determining that the vehicle enters a subsequent location, the energy consumption estimation model is updated with a location model received from an edge/cloud server, to estimate an energy range along the route through the subsequent location.

Customized electrical vehicle range estimation system based on location and driver

Systems include a controller programmed to instruct a sensor to detect an object in an environment of a vehicle in response to an activation of a fall-on-car prevention function, determine a first value of a parameter pertaining to the object at a first time with the sensor, determine a second value of the parameter pertaining to the object at a second time with the sensor, and automatically cause an action to occur based on a comparison of the first value of the parameter and the second value of the parameter.

Systems and methods for detecting an object in an environment of a vehicle

Systems, methods, and other embodiments described herein relate to improving the performance of a device in different geographic locations by using transfer learning to provide a customized learning model for the different locations. In one embodiment, a method includes receiving segments of a model from separate members in a geographic hierarchy and assembling the segments into the model. The segments include at least a first segment, a second segment, and a third segment. The method includes processing sensor data using the model to provide an output for assisting a device.

Hierarchical transfer learning system

A trailer monitoring system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to receive, from at least one monocular camera mounted to a vehicle towing a trailer, at least one monocular camera image of at least a portion of the trailer. The instructions also cause the processor to generate at least one depth map based on the at least one monocular camera image. The instructions further cause the processor to identify a trailer irregularity based on the at least one depth map.

Trailer monitoring system

An object detection system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to receive a priority assignment set by an occupant of a vehicle. The priority assignment corresponds to a customization of at least one warning characteristic. The at least one warning characteristic includes a distance threshold. The instructions also cause the processor to detect an object in an external environment of the vehicle and apply the priority assignment to the object. The instructions further cause the processor to issue a warning according to the at least one warning characteristic. According to the distance threshold, the warning is issued when a distance from the vehicle to the object meets the distance threshold.

Object detection system

Systems and methods are provided for improving the performance of anomaly detection, including detecting abnormal driving behavior at locations and in driving situations where the accuracy of detecting abnormal driving behavior has decreased or known to cause inaccurate performance responses. For example, the anomaly detection mechanism may detect abnormal driving behavior (e.g., delayed responses, road rage, swerving, etc.) or environmental anomalies (e.g., potholes, fallen trees, etc.), and particular driving situations or locations can have repeatedly shown decreased rates of accuracy with responding to these anomalies, including abnormal driving behavior (e.g., delayed driver responses to left-turns, U-turns, traffic light, railroad crossing, construction zones, etc.) or situations that are known to cause inaccurate performance responses (e.g., potholes, fallen trees, etc.). Embodiments can revise the anomaly detection system to operate the vehicle more consistently with other human drivers.

Systems and methods to improve the performance of anomaly detection

Compensation can be made for mismatch in abnormal driving behavior detection between an ego vehicle and its human driver. Sensor data of a surrounding driving environment can be acquired. The acquired sensor data can be analyzed to detect abnormal driving behavior by a vehicle in the surrounding driving environment. Responsive to detecting abnormal driving behavior by the vehicle in the surrounding driving environment, the abnormal driving behavior can be classified as to whether it is severe. If the abnormal driving behavior is classified as not being severe, an amplified version of the abnormal driving behavior can be caused to be presented to the human driver of the ego vehicle.

Compensating mismatch in abnormal driving behavior detection

The disclosure generally relates to a system comprising a memory and a processor configured to access the memory and execute the machine-executable instructions stored on the memory to determine that a target vehicle is engaging in an abnormal driving, determine that the abnormal driving exceeds a notification threshold parameter of an abnormal driving notification system, generate a notification, and monitor the ego vehicle and/or driver to alter notification threshold values based on the reactions of the ego vehicle and/or driver inputs.

Customizable abnormal driving detection

The disclosure includes implementations for providing ground adjustment for an in-vehicle augmented reality system. A system may include a three-dimensional heads-up display unit ('3D HUD') installed in a vehicle. The system may include a memory storing instructions that, when executed, cause the system to: determine a plurality of elevation values for a plurality of points on a road surface, where each elevation value is associated with a point from the plurality of points and describes the elevation of that point; identify a graphic for display on the 3D HUD, where the graphic is associated with at least one point from the plurality of points; determine which location of the 3D HUD is associated with the at least one point associated with the graphic; and display the graphic at the location of the 3D HUD so that the graphic superposes the point when viewed by a driver.

Growing community of inventors

Mountain View, CA, United States of America

Emrah Akin Sisbot