A vehicle includes a sensor circuit configured to detect an obstacle in a first region which is located on the predetermined traveling route and in a second region which is adjacent to the first region on the predetermined traveling route, the second region being farther than the first region. The vehicle enters the first region in a case where: there is no obstacle in the first region; and there is no obstacle in the second region, and does not enter the first region and stops before the first region in a case where: there is no obstacle in the first region; and there is an obstacle in the second region.

A vehicle control device includes an oncoming vehicle detection sensor configured to detect an oncoming vehicle that approaches an own vehicle, and a controller configured to automatically apply brakes to the own vehicle to avoid a collision with the oncoming vehicle detected by the oncoming vehicle detection sensor under a condition that the own vehicle is at least partially in an opposite lane or a planned path of the own vehicle is at least partially in the opposite lane. The controller is configured to set, between the own vehicle and the oncoming vehicle, a virtual area that moves with the oncoming vehicle and that extends in an advancing direction of the oncoming vehicle, and automatically brake the own vehicle to avoid coming into contact with the virtual area to avoid the collision between the own vehicle and the oncoming vehicle.

An early-warning system, method, and apparatus for steering of a two-wheeled vehicle, and a corresponding two-wheeled vehicle including a display apparatus. The system includes: a first control system and a second control system both operating independently and exchanging data between each other in real time; the first control system is configured to: monitor a steering state of the two-wheeled vehicle in real time and generate steering state data; generate early-warning state data based on the steering state data; transmit at least part of the steering state data and the early-warning state data to the second control system in real time; and control corresponding components of the two-wheeled vehicle to operate according to control instructions received from the second control system and internal preset instructions; and the second control system is configured to: control a display of the display apparatus based on the steering state data and/or the early-warning information.

A navigation system for a host vehicle may include a processor programmed to: receive, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment to be traversed by the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, the point cloud information being representative of distances to various objects in an environment of the host vehicle; compare the received point cloud information with at least one of the plurality of mapped navigational landmarks in the sparse map to provide a LIDAR-based localization of the host vehicle relative to at least one target trajectory; determine an navigational action for the host vehicle based on the LIDAR-based localization of the host vehicle relative to the at least one target trajectory; and cause the at least one navigational action to be taken by the host vehicle.

A system for controlling platooning by a following vehicle includes a sensor located in or on the following vehicle configured to detect data corresponding to a shape of a leading vehicle. The system further includes an electronic control unit (ECU) located in or on the following vehicle, coupled to the sensor, and configured to determine an optimal distance from the following vehicle to the leading vehicle based on the shape of the leading vehicle, the optimal distance corresponding to a distance at which drag applied to the following vehicle is reduced based on a pressure wake from the leading vehicle.

Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

A method for monitoring uncertainty for human-like behavioral modulation of trajectory planning includes: retrieving map and agent information of a current driving state of an autonomously operated host automobile vehicle; dividing uncertainty conditions affecting a trajectory of the host automobile vehicle into an expected uncertainty and an unexpected uncertainty; calculating the expected uncertainty in a first operation branch by forming attention zones according to identified portions of lanes which may potentially collide with a planned route of the host automobile vehicle; determining the unexpected uncertainty in a second operation branch by calculating an anomaly score for any other vehicles in a surrounding area of the host automobile vehicle positioned in the lanes which may potentially collide with the planned route of the host automobile vehicle; and modulating trajectory operation signals determined for the expected uncertainty if the unexpected uncertainty meets or exceeds a predetermined threshold.

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

A vehicle collision avoidance assist apparatus keeps stopping a collision avoidance control to avoid a collision of an own vehicle with an object ahead of the own vehicle when the own vehicle turns, a predetermined condition is satisfied, and a collision condition is satisfied. While the own vehicle turns, the apparatus acquires an own vehicle turning angle which is an angle which the own vehicle has turned about a turning center from when the own vehicle starts turning and change the predetermined condition, depending on the own vehicle turning angle.

An apparatus and a method in an autonomous vehicle detect a traffic flow obstruction target. The apparatus detects information about at least one of a speed of another vehicle, a driving path of the other vehicle, or a position of the other vehicle. The apparatus calculates a degree to which the other vehicle interferes with traffic flow, based on the detected information and based on high definition map information stored in a memory. The apparatus selects a traffic flow obstruction target, based on the degree to which the other vehicle interferes with the traffic flow. The apparatus detects a target causing bypass driving, which is present on a driving path, to enhance the continued operation of autonomous driving.