Systems and methods for managing speed thresholds for a fleet of vehicles are disclosed. Input is used to provide associations between particular weather-relation conditions (such as rain) and arithmetic operations, that may be used to determine a current speed threshold as a function of a local posted speed limit at the current location of a vehicle. The current speed threshold is subsequently used to detect whether vehicles are exceeding the current speed threshold.

Provided herein is a system and method of a vehicle. The system comprises one or more sensors, processors, maps, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: monitoring a location of the vehicle while driving; detecting a sign while the vehicle is driving; capturing, frame-by-frame, data of the sign until the sign disappears from a field of view of the sensor; synchronizing each frame of the data with the location of the vehicle; determining a location of the sign based on the frame-by-frame data; in response to determining, at a frame immediately before the sign disappears from the field of view of the sensor, that the vehicle is driving towards the sign, uploading the detected sign and the location of the sign onto the one or more maps; and implementing a driving action based on the sign.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

A method for providing driving assistance based on a HD map includes acquiring information such as position of a vehicle and timing, and acquiring a speed of the vehicle based on the manner of driving, and acquiring distance an intersection in front of the vehicle and information from a HD map such as traffic light intersection ahead. Deciding such as whether the vehicle can pass through the intersection based on the speed of the vehicle, intersection distance, and the traffic light information and prompting and/or controlling the vehicle if it is determined that passage through is not possible. A vehicle-mounted apparatus applying the method is also disclosed.

According to an embodiment, an information processing device includes a memory and processing circuitry. The processing circuitry is configured to acquire a map defining a target in a coordinate space in which a direction along a traveling direction of a moving object is one of coordinate axes, and approximate a movement route of the moving object with a specified shape area along a coordinate axis in the coordinate space, the specified shape area being a basic unit of collision determination.

Included are: a captured image acquiring unit to acquire a captured image obtained by imaging an occupant; a temperature image acquiring unit to acquire a temperature image indicating a temperature of a surface of a body of the occupant measured in a non-contact manner; a motion detection unit to detect a motion of the occupant on the basis of the captured image; a temperature detection unit to detect a temperature of a hand of the occupant on the basis of the temperature image; and an awakening level estimating unit to estimate an awakening level of the occupant on the basis of the motion of the occupant detected by the motion detection unit and the temperature of the hand of the occupant detected by the temperature detection unit.

Techniques are disclosed herein for providing guidance for autonomous vehicles in areas of low network connectivity, such as rural areas. According to an embodiment, a guidance system receives a request to exchange data with a vehicle within a specified radius thereof over a wireless connection (e.g., a radio frequency protocol-based connection). The data is stored by the guidance system and is indicative of navigation information within the specified radius. The guidance system transmits the stored data to the vehicle. The guidance system also receives, from the vehicle, data indicative of navigation information for a path previously passed by the vehicle.

Surface penetrating radar interrogates a region adjacent a pathway of the vehicle in response to activation by a user. An object detection system which is responsive to the radar transceiver is configured to recognize one or more spatial signatures of one or more detected objects in the region. A controller coupled to the radar transceiver and the object detection system is configured to (i) compare a respective spatial signature of at least one of the detected objects to a plurality of predetermined target signatures to detect an infrastructure asset, (ii) assess a perimeter around the detected infrastructure asset to estimate a severity of an obstruction blocking the infrastructure asset, and (iii) convey an alert message to the user when the estimated severity is greater than a threshold.

A traffic safety control method is provided. The method includes determining whether there is an intersection in front of a vehicle. When there is the intersection in front of the vehicle, a driving direction of the vehicle at the intersection is predicted according to a plurality of lane positions of the vehicle that are continuously obtained. A message is transmitted once the predicted driving direction is determined to be conflicting with a traffic rule of the intersection.

The control unit 15 of the in-vehicle device 1 is configured to acquire point cloud data outputted by the lidar 2. Then, the control unit 15 is configured to associate, through matching between the acquired point cloud data and voxel data that is position information of an object for each of unit areas (voxels) into which a space is divided, measurement points constituting the point cloud data with voxels, respectively. The control unit 15 performs the position estimation of a moving body equipped with the lidar 2 based on the measurement points associated with any of the voxels which have corresponding voxel data VD and the position information of the object for the associated voxels. The control unit 15 calculates a reliability index of a position acquired by the position estimation based on a DAR that is a ratio of the number of the measurement points associated with any of the voxels to the number of the measurement points constituting the point cloud data.