Provided is an advanced driver assist system (ADAS) and a vehicle having the same. The ADAS includes: a communicator configured to communicate with a plurality of other vehicles; an obstacle detector configured to detect an obstacle in a surrounding and output obstacle information about the detected obstacle; and a controller configured to acquire distance information about a distance to a second vehicle travelling in the surrounding of the first vehicle among the obstacles based on the obstacle information detected by the obstacle detector during a cruise control mode, acquire travel information and position information of a third vehicle travelling in the surrounding of the second vehicle based on information received through the communicator, and controlling acceleration and deceleration based on the distance information with respect to the second vehicle, the travel information of the third vehicle, and the position information of the third vehicle.

An apparatus for assisting driving of a vehicle includes: a radar mounted on the vehicle to have a front field of view of the vehicle and configured to acquire detection data; and a controller including a processor, configured to process the detection data, and configured to identify an estimated collision time between the vehicle and a first preceding vehicle, located in front of the vehicle, based on processing of the detection data, and to control a braking system of the vehicle to brake the vehicle, in response to the estimated collision time being less than a reference time, wherein the controller is configured to increase a reference time for braking the vehicle, based on an acceleration and a travelling speed of the first preceding vehicle and an acceleration and a travelling speed of a second preceding vehicle, located in front of the first preceding vehicle.

A route information storage function having decreased data storage requirements is provided in a vehicle controller that includes a processor, a first storage unit, and a second storage unit and that stores route information indicating a route to a target point. The vehicle controller includes a traveling state acquiring unit that acquires route information on a vehicle, a short-term storage information processing unit that stores the route information in the first storage unit, as short-term storage information, the route information being acquired by the traveling state acquiring unit while the vehicle is traveling, and a long-term storage information processing unit that after the vehicle has reached the target point, determines long-term storage information from short-term storage information stored in the first storage unit, the long-term storage information processing unit storing the determined long-term storage information in the second storage unit.

This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

A driving support device includes a storage device storing a program and a hardware processor. The hardware processor executes the program stored in the storage device to perform driving support of a vehicle based on a detection result of at least one of a radar device and an imaging device mounted in the vehicle, determine whether the vehicle is traveling in an underpass which is a traffic route along which the vehicle is able to pass under an overlying structure, and suppress an operation of the driving support when the vehicle is determined to be traveling under the underpass.

Systems and methods for assigning a lane to an object in an environment of an autonomous vehicle are disclosed. The methods include assigning an instantaneous probability to each of a plurality of lanes in the environment based on a current state of the object, generating a transition matrix for each of the plurality of lanes, and identifying the lane in which the object is moving at the current time t based on the instantaneous probability and the transition matrix. The instantaneous probability is a measure of likelihood that the object is in that lane at a current time. The transition matrix encodes one or more probabilities that the object transitioned either into that lane or out of that lane at the current time.

The present disclosure relates to a driving control system and a method of controlling the same using sensor fusion between vehicles, and the driving control system allows vehicles to share sensor data, fuses the sensor data, matches the adjacent vehicle and the sensor data, improves recognition performance in respect to a periphery, controls driving in accordance with a change in peripheral environment and a traveling state of another vehicle, receives sensor data of another vehicle, converts a coordinate of sensor data of the matched vehicle, and fuses host vehicle sensor information, which makes it possible to improve recognition performance and accuracy in respect to a surrounding environment and object, enable stable autonomous driving, and improve stability of the vehicle.

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

The present disclosure adds automotive millimeter-wave (mmWave) radars to the current perception system and makes them an effective augmentation for ultrasonic sensors (USSs). Relying on the superior range and Doppler resolution, mmWave radars generate denser point clouds than the intersection detections of USSs, making it possible to form a more robust and accurate radar occupancy grid. The radar occupancy grid can be formulated as a polygon with multiple nodes. Thus,, the memory-consuming occupancy grid is simplified as a polygon that consists of a bunch of points, which can be used in the downstream application for relieving computational burden. Radars measure and estimate the Doppler velocity of detected targets such that one can assign a moving velocity to each node of the radar polygon. This makes it possible to predict the shape of a future radar polygon and feed the predicted radar polygon to downstream applications.

A method and a system for generating a mesh representation of a surface. The method includes receiving a three-dimensional (3D) point cloud representing the surface, generating a reconstruction dataset having a higher resolution than the 3D point cloud in one or more regions corresponding to the surface from the 3D point cloud, and generate a polygon mesh representation of the surface by using a fine-to-coarse hash map for building polygons at a highest resolution first followed by progressively coarser resolution polygons, using the reconstruction dataset.