A vehicle includes a plurality of zone control units that each comprise an inertial measurement unit, and wherein each zone control unit is configured to provide inertial measurement data obtained from its respective inertial measurement unit to other vehicle components via a vehicle bus.

The present invention relates to a collision distance estimation device and a driver assistance system using the same. The collision distance estimation device includes an image acquisition unit configured to acquire images of surroundings of a vehicle to generate image information, an image reading unit configured to detect and identify an object present around the vehicle from the image information to generate object recognition information, a travel detection unit configured to generate movement distance information on the basis of wheel sensing information, steering information, and the image information, and a collision distance calculation unit configured to calculate collision distance information on the basis of the object recognition information and the movement distance information.

The present invention relates to a collision distance estimation device and a driver assistance system using the same. The collision distance estimation device includes an image acquisition unit configured to acquire images of surroundings of a vehicle to generate image information, an image reading unit configured to detect and identify an object present around the vehicle from the image information to generate object recognition information, a travel detection unit configured to generate movement distance information on the basis of wheel sensing information, steering information, and the image information, and a collision distance calculation unit configured to calculate collision distance information on the basis of the object recognition information and the movement distance information.

A device and a method for controlling autonomous driving control a speed of an autonomous vehicle before downhill travel. The device and method calculate a travel resistance of an autonomous vehicle on a travel-intended-route, including a downhill route, a main braking pressure required to travel at a constant speed, and a brake temperature based on braking. The device and method determine whether to reduce the main braking pressure based on the calculated brake temperature and calculates a decreased amount of the main braking pressure and an increased amount of a speed of the autonomous vehicle based on the decreased amount of the main braking pressure on the travel-intended-route when determining to reduce the main braking pressure. The device and method limit a maximum speed of the autonomous vehicle before entering the travel-intended-route based on the increased speed amount.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by an edge reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The edge reversing-determination machine-learning model being updated based on a centralized reversing-determination machine-learning model trained using a vehicle-provided reversing indication.

An apparatus for controlling backward driving of a vehicle including: a driving trajectory generation unit configured to generate a driving trajectory for backward driving of an ego vehicle on a target path, using sensing information acquired while the ego vehicle drives forward along the target path; and a control unit configured to control the backward driving of the ego vehicle on the target path according to the driving trajectory generated by the driving trajectory generation unit, correct the driving trajectory using driving information of another vehicle, which has driven backward on the target path before the ego vehicle, when a change on the target path is sensed in comparison to during the forward driving of the ego vehicle during the process of controlling the backward driving of the ego vehicle, and control the backward driving of the ego vehicle according to the corrected driving trajectory.

A tire slip state determination method includes: detecting rotation fluctuations of a power transmission member and a wheel body of a wheel; determining, based on an amplitude ratio of a rotation fluctuation amplitude of the wheel body to a rotation fluctuation amplitude of the power transmission member and a phase delay of the rotation fluctuation of the wheel body relative to the rotation fluctuation of the power transmission member, whether a vibration mode of the wheel body and the tire is an elastic slip mode or a sliding slip mode; and determining that the tire is in the sliding slip state when the vibration mode is the sliding slip mode. The amplitude ratio and the phase delay are calculated by using, as a tire driving radius, an effective rolling radius in a region in which a relationship between a dynamic load radius and the effective rolling radius is linear.

Embodiments of this application provide a method and an apparatus for determining drivable region information. The method includes obtaining first information, where the first information includes information about an initial drivable region determined based on at least one image, and the at least one image is from at least one camera module. The method also includes obtaining second information, where the second information includes radar detection information. The method further includes determining first drivable region information based on the first information and the second information.