A vehicle sensing system is for being disposed on a vehicle. The vehicle sensing system includes a calculating unit, which includes a turning calculating module and a vehicle dimension dataset. The vehicle dimension dataset includes at least one of a wheelbase, a vehicle width, a front overhang and a rear overhang of the vehicle. The calculating unit is configured to receive a turning dataset of the vehicle. Based on the turning calculating module, the calculating unit is configured to determine an inner front wheel and an inner rear wheel. The calculating unit is configured to further determine a turning alarm zone in accordance with the vehicle dimension dataset and the turning dataset. The turning alarm zone is dependent on at least one of time and the turning dataset.

Disclosed is a vehicle including a camera having an external field of view of the vehicle and obtaining image data. The vehicle further includes a controller including a processor configured to process the image data. In particular, the controller is configured to determine a target vehicle from at least one surrounding vehicle by processing the image data, determine a first reference point and a second reference point based on the target vehicle, and control the vehicle to follow the target vehicle based on the first reference point and the second reference point.

System, methods, and other embodiments described herein relate to emulating vehicle dynamics. In one embodiment, a method for emulating vehicle dynamics in a vehicle having a plurality of wheels and equipped with all-wheel steering, includes receiving emulation settings that indicate one or more environment parameters and/or vehicle parameters, detecting driver inputs including at least steering input and throttle input, executing a simulation model that receives the driver inputs and emulation settings, simulates the vehicle operating based on the driver inputs and the emulation settings, and outputs one or more simulated states of the vehicle based on the simulated operation of the vehicle, determining one or more actuation commands for each wheel of the vehicle to cause the vehicle to emulate the one or more simulated states, and executing the one or more actuation commands, wherein the actuation commands include at least wheel angle commands and torque commands.

Autonomous driving is possible even in a no-lane section. Probe information to be transmitted from a plurality of vehicles is stored. Vehicle travel trajectory information in a no-lane section is extracted from the stored probe information, the vehicle travel trajectory information is extracted per combination of an entry point and an exit point for the no-lane section, the extracted vehicle travel trajectory information per combination of the entry point and exit point for the no-lane section is sorted into a plurality of categories according to a predetermined criterion, and statistical trajectory information is calculated by statistical processing of the travel trajectory information for each of the plurality of categories. The calculated statistical trajectory information is transmitted to the plurality of vehicles as vehicle route information in the no-lane section.

A method for monitoring driving behavior risk degree, comprises the steps of: acquiring first vehicle size information of a first vehicle, second vehicle size information of a second vehicle, vehicle driving information and vehicle driving environment information; determining the number of overlapping regions of the first vehicle and the second vehicle based on the first vehicle size information, the second vehicle size information and the vehicle driving information; and determining risk degree information of driving behavior based on the number of overlapping regions, the vehicle driving information and the vehicle driving environment information. This method may be applied in multiple scenarios, and facilitates drivers or passengers in avoiding them from traffic collision incidents, so that the safeties of the drivers and the passengers is greatly improved.

The present disclosure relates to a vehicle control method, a vehicle control device, and a vehicle control system including same. Specifically, a vehicle control device according to the present disclosure includes: a vehicle travel route estimation unit which estimates a first travel route of a vehicle on the basis of the turn radius and traveled distance of the vehicle, when the speed of the vehicle is equal to or less than a preset speed; a target travel route predicter which confirms a target from periphery detection information about the periphery of the vehicle, and predicts a second travel route of the target; a collision determiner which determines whether there is a possibility of a collision between the vehicle and the target; and a vehicle control unit which controls to perform at least one among an alert control, a brake control, and an avoidance control, when there is a possibility of a collision between the vehicle and the target.

In some embodiments, techniques for updating a model that represents turning dynamics of a vehicle are provided. In some embodiments, an initial location and orientation of the vehicle with respect to an object outside the vehicle are determined using information from environment sensors including but not limited to image sensors and/or range sensors. In some embodiments, vehicle state information including but not limited to steering angle and wheel speed is received before determining a new location and orientation of the vehicle with respect to the object. In some embodiments, the model is updated using the initial location and orientation, the new location and orientation, and the vehicle state information.

A system for avoiding blind spot using accident history information, includes an image sensor configured to provide image information by acquiring a surrounding image of a host vehicle, and a vehicle controller. The vehicle controller is configured to detect, through the image sensor, an adjacent vehicle traveling adjacent to the host vehicle and a license plate of the adjacent vehicle; determine a dangerous level of the blind spot of the adjacent vehicle, based on accident history information of the adjacent vehicle and driver tendency information of a driver of the adjacent vehicle obtained by inquiring about the license plate of the adjacent vehicle, after determining a blind spot range of the adjacent vehicle; and generate a path in which the host vehicle deviates from the blind spot or avoids the blind spot to reduce the dangerous level of the blind spot, based on a traveling situation of the host vehicle.

A driving assistance apparatus for a vehicle includes a surrounding environment information acquirer that acquires environment information about a surrounding of the vehicle, a leading-vehicle detector that detects a leading vehicle based on the environment information, a moving-body detector that detects a moving body in the surrounding of the vehicle based on the environment information, a vehicle-speed detector that detects a vehicle speed of the vehicle, and a region setter that sets a cut-in detection region for detecting entrance of the moving body between the vehicle and the leading vehicle when a travel controller causes the vehicle to travel such that the vehicle follows the leading vehicle. The region setter sets a protruding region that is included in the cut-in detection region based on at least the vehicle speed such that a length of protrusion in a left-right direction of the protruding region increases as the vehicle speed decreases.

A system for representing objects in a surrounding environment of a vehicle using a Frenet coordinate system. The system includes a memory and an electronic processor. The electronic processor is in communication with the memory and configured to, receive, from one or more sensors, input regarding one or more objects in the surrounding environment. The input includes, for each of the one or more objects, a representation of a location of the object in Cartesian coordinates. The electronic processor is further configured to convert the location of each object from Cartesian coordinates to Frenet coordinates and store the location of each object in Frenet coordinates in the memory.