Toyota Motor Engineering & Manufacturing N.A. Inc. (Plano, TX)

A method is based on the reception of sensor data by one or more sensors of vehicles, in which the sensor data is associated with the environment of the vehicle, and detecting an object based on the sensor data, determining pixel coordinates for the object based on the data from the sensor, then converting the pixel coordinates of an object into world coordinates of the object, extracting the features that are associated with the object, and transmitting the feature set and the world coordinates to a remote computing device.

Autonomous vehicles require an intelligent control system in order to perceive the environment, manage the mechanical components, plan routes, and other functions. The ability to perceive the environment depends onvarious sensors on autonomous vehicles, such as cameras and GPS sensors. Autonomous vehicles may also utilize high-definition maps (HDM), which can offer a precise and high-quality representation (e.g. intersections, lanes, crosswalks, etc.). The ability to see the surrounding environment and carry out autonomous driving can be enhanced through the use of pre-calculated information of the HD map.

An HD map could comprise multiple layers, including the base layer, a geometric map layer, a semantic layer, a map prior layer, and an active layer. The 3D environment information, including the raw sensor data of LiDAR sensors or cameras, may be contained in the layer of geometric maps. The semantic layer may contain semantic objects like a lane boundary, intersections and other such. The dynamic layer can include dynamic information such as the observed speed or congestion. These layers provide crucial data for autonomous vehicles and significantly enhance autonomous driving performance.

However, the majority of sensors for vehicles, including LiDAR sensors and cameras, are line-of-sight based. Therefore, they cannot record information from the surrounding environment that isn’t within an area of view. A big truck can hinder the view of the camera mounted to the vehicle’s camera, as an instance. In addition, sensors could be affected by unpredicted circumstances that may arise during real-world driving. Incomplete sensor data can affect the driving efficiency of autonomous vehicles. One example is that autonomous vehicles may not be able to recognize pedestrians if it is equipped with inadequate sensor data. Accordingly, aneed exists for strategies and techniques to increase the capability of autonomous vehicles to sense their environment.

One method involves receiving sensor data with one or more sensors of an automobile, finding objects using the sensor data, and then converting them to the world’s coordinates. The object’s features can then be extracted and transmitted to distant computing devices. The sensor data could be associated with the surroundings that is part of the vehicle.

In a different manner the method could comprise receiving at an unconnected computing device object data from a number of vehicles, the object data comprising the location of an object that is recognized by the plurality of vehicles, determining an estimatedlocation of the object based upon the locations of the object that are detected by the plurality of vehicles, and broadcasting the estimated location of the object to each vehicle in the plurality.

In another example, a vehicle system may include one or more processors and memory modules, or one or more sensors that receive information from sensors that are associated with the automobile’s environment as well as machine-readable instructions stored in one or several memory modules. When executed by processors, the machine-readable instructions could cause the system to detected an object using the sensor data, identify pixel coordinates of the object using the sensor data, convert the coordinates of the pixel of the object to world coordinates of the object, extract a set of features associated with the object, and transmit the set of features and the world coordinates of the object to remote computing devices.

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