Disclosed are aspects of a radar system for a vehicle that includes a radar circuit for generating and processing radar signals, wherein the radar circuit includes a ground plane connector for an electrical connection with an antenna ground plane. The radar system also includes a radar antenna assembly for transmitting radar signals into a traffic space and for receiving radar signals reflected by objects present in the traffic space. The radar system further includes a component of the vehicle. The ground plane connector is electrically connected to the component of the vehicle.

Provided are methods for efficient vehicle extent estimation, which can include bounding box generation. Some methods described include determining bounding boxes surrounding detected point clusters according to tangents to convex hulls of the point clusters, and minimizing continuous functions of distances between points and bounding box sides. Accordingly, best-fit bounding boxes are determined more efficiently and quickly, as well as more accurately. Systems and computer program products are also provided.

A computer-implemented method comprises: generating first output using a first sensor of a vehicle comprising an infrared camera or an event-based sensor, the first output indicating a portion of surroundings of the vehicle; providing the first output to a LiDAR of the vehicle having a field of view (FOV); configuring a resolution of the LiDAR based at least in part on the first output; generating a representation of at least part of the surroundings of the vehicle using the LiDAR; providing, to a perception component of the vehicle, second output of a second sensor of the vehicle and third output of the LiDAR, the perception component configured to perform object detection, sensor fusion, and object tracking regarding the second and third outputs, wherein the first output bypasses at least part of the perception component; and performing motion control of the vehicle using a fourth output of the perception component.

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

The present disclosure relates to an apparatus and method for controlling an autonomous vehicle to allow an autonomous vehicle to safely pass through a road according to a driver's choice when the width of the road is narrow. The apparatus includes a sensor for acquiring information data of obstacles and vehicles in front of and on a side of a host vehicle, a signal processor for outputting data with respect to positions and media of obstacles and a determination signal representing presence or absence of a vehicle on a driving path, a controller for determining whether driving is possible by analyzing information acquired by the sensor and outputting a control signal corresponding to a selection signal of the driver, an interface for displaying an image processed by the signal processor, and an autonomous driving function unit for performing autonomous driving according to the control signal.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

A method for providing assistance in driving includes capturing an image of a second moving vehicle when a first moving vehicle is moving and obtaining basic information of the second moving vehicle according to the image thereof, the basic information of the second moving vehicle comprising weight information of the second moving vehicle. Driving information of the first moving vehicle is obtained, and a safe distance between the first moving vehicle and the second moving vehicle is determined according to the driving information of the first moving vehicle and the basic information of the second moving vehicle. The current distance between the first moving vehicle and the second moving vehicle is detected, and a warning is output if the distance between the first moving vehicle and the second moving vehicle is less than the safe distance.

A control unit 15 of an in-vehicle device 1 configured to acquire, from landmark data LD that is map data including position information of one or more features, plural pieces of position information of a feature which is drawn on a road surface and which exists at or around a vehicle. Then, the control unit 15 is configured to calculate a normal vector of an approximate plane calculated based on the acquired plural pieces of the position information. Then, the control unit 15 is configured to calculate at least one of a pitch angle or a roll angle of the vehicle based on the orientation of the vehicle and the normal vector.

Techniques for determining distortion in a map caused by measurement errors are discussed herein. For example, such techniques may include implementing a model to estimate map distortion between the map frame and the inertial frame. Data such as sensor data, map data, and vehicle state data may be input into the model. A map distortion value output from the model may be used to compensate vehicle operations in a local region by approximating the distortion as linearly varying about the region. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory.

Systems and methods for operating a mobile platform. The methods comprise, by a computing device: obtaining a LiDAR point cloud; using the LiDAR point cloud to generate a track for a given object in accordance with a particle filter algorithm by generating states of a given object over time (each state has a score indicating a likelihood that a cuboid would be created given an acceleration value and an angular velocity value); using the track to train a machine learning algorithm to detect and classify objects based on sensor data; and/or causing the machine learning algorithm to be used for controlling movement of the mobile platform.