Systems and methods are provided for implementing personalized adaptive cruise control techniques in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with one embodiment, a method comprises receiving first vehicle operating data and associated first environmental data of a plurality of vehicles; classifying the first vehicle operating data and the first environmental data into a plurality of driver type classifications; training a control policy model for each driver type classification based on the first vehicle operating data and the first environmental data; receiving a real-time classification of a target vehicle based on second vehicle operating data and associated second environmental data of the target vehicle; and output a trained control policy model the to target vehicle based on the real-time classification of the vehicle, wherein the target vehicle is controlled according to the trained control policy model.

A vehicle includes a controller that identifies a target around the vehicle and calculates a danger range of the identified target, based on processing surrounding data obtained by sensor devices; calculates a danger range of the vehicle based on processing driving data obtained by sensor devices; determines a danger of collision based on the danger range of the target and the danger range of the vehicle, and control a driving apparatus based on the determined danger of collision. Such a vehicle and a control method thereof can make it possible to avoid a collision based on a danger range by calculating the danger range between the vehicle and a surrounding object of the vehicle depending on a factor causing uneasiness of a user.

Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

A system and method for reducing the risk of road accidents on account of blind spot errors and a vehicle using the system and method includes a visual sensing unit, the visual sensing unit comprising a first camera and a second camera, wherein the first camera looks left and obtains a first image information, the second camera looks to the right and obtains a second image information; a pre-processing unit, the pre-processing unit being coupled with the visual sensing unit, wherein the pre-processing unit processes the first image information and the second image information to generate a single image. An image processing unit generates an obstacle recognition information according to the processed image.

A method for operating an accelerator pedal-controlled distance controller of a vehicle. The distance controller regulates a distance to a target vehicle as a function of an actuator pedal value of the vehicle and activates automatic braking operations as necessary. A braking operation is aborted when the accelerator pedal value is increased during the braking operation.

A collision detection method, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of intelligent transportation and autonomous driving technologies. The method includes: determining a predicted travel range of a target object based on a planned travel trajectory of the target object and a historical travel trajectory of the target object; determining, in response to a target obstacle being detected, a predicted travel range of the target obstacle based on a current travel state of the target obstacle; and determining whether the target object has a risk of colliding with the target obstacle, based on the predicted travel range of the target object and the predicted travel range of the target obstacle.

Aspects of the disclosure may enable autonomous vehicles to respond to emergency vehicles. For instance, sensor data identifying an emergency vehicle approaching the autonomous vehicle may be received. A predicted trajectory for the emergency vehicle may be received. Whether the autonomous vehicle is impeding the emergency vehicle may be determined based on the predicted trajectory and map information identifying a road on which the autonomous vehicle is currently traveling. Based on a determination that the autonomous vehicle is impeding the emergency vehicle, the autonomous vehicle may be controlled in an autonomous driving mode in order to respond to the emergency vehicle.

A method for evaluating a minimum breaking distance of a vehicle, in particular a car. The method comprises the step of obtaining at least one image in a movement direction of the vehicle associated substantially with an actual location of vehicle. A first road type indication from the at least one image is determined by a trained neural network architecture. Second road type indication associated with the actual location of the car are obtained from a database and compared with the first road type indication. If the second road type indication supports the determined first road type indication, an adjustment parameter associated with one of the at least first and second road type indication is selected. If second road type indication does not support the determined first road type indication, a default adjustment parameter as adjustment parameter is selected. Finally, a minimum breaking distance using the adjustment parameter is set.

A control system of a vehicle that can travel in a first state in which travel control is performed based on a position of a white line on a travel lane and in a second state in which travel control is performed based on a travel position of another vehicle. Periphery information is obtained of the vehicle. It is determined, based on the periphery information obtained, whether an emergency vehicle is approaching. A control unit configured to perform control so that travel control in the first state is prioritized when it is determined that the emergency vehicle is not approaching. Travel control in the second state is prioritized when it is determined that the emergency vehicle is approaching.

An alert control apparatus includes: an alert control unit to issue a first alert to an occupant in a moving body if an object in a particular category is present within a region to which the moving body is headed, and issue a second alert to the occupant if an object in a category other than the particular category is present within the region; a reception control unit to perform, when the moving body enters a new movement section on a movement route, control for receiving a category of an object present within the new movement section from an external apparatus. If there is an object for which an alert is to be issued during movement within the new section, the alert control unit, based on the category received from the external apparatus, controls as to which of the first and second alerts is to be issued.