For the operation of a motor vehicle, according to the present disclosure, situation variables are detected using a number of environment sensors, a current driving situation is derived from the number of situation variables using a controller and a number of control inputs used to control the motor vehicle are detected, and a behavior pattern for a specific driver is determined using the controller on the basis of the control inputs, is assigned to the current driving situation, and is stored in a memory unit of the controller. Should it be concluded on the basis of the situation variables that the view is limited, the lack of certainty of the behavior pattern determined for the current driving situation is compared with that of a behavior pattern for the same driver determined for a driving situation with an unrestricted view, and the use of a system to improve the view is recommended to the driver if a lack of certainty on the part of the driver is identified on the basis of the comparison.

Embodiments of the present disclosure relate to vehicle technologies, and disclose a method and system for controlling a vehicle, and a fingerprint chip. The method comprises: collecting a fingerprint image of a user; authenticating the user according to the fingerprint image and acquiring identity information of the user after the authentication succeeds; acquiring a vehicle personalization parameter of the user according to the identity information of the user and setting up the vehicle according to the vehicle personalization parameter. The embodiments further provide a vehicle control system and a fingerprint chip. The technical solutions provided by the embodiments of the present disclosure can allow simple and quick personalized settings of a vehicle based on identity recognition, which can facilitate sharing a vehicle among family members in most cases.

In some embodiments, a range sensor is configured to detect a distance between a portion of a vehicle and an object above the portion of the vehicle. In some embodiments, the detected distance may be presented to an operator to allow the operator to control a height of an adjustable suspension in order to manually control the distance. In some embodiments, the detected distance may be used to automatically control the distance. In some embodiments, the distance may be controlled in order to allow the vehicle to couple to the object, such as a fifth wheel of the vehicle coupling to a kingpin of a trailer.

This disclosure is generally directed to systems and methods for avoiding certain undesirable vehicle operations when a vehicle having a dual vehicle propulsion system is parked inside a confined space. For example, it is desirable to avoid operating an internal combustion engine of a hybrid electric vehicle when the hybrid electric vehicle is parked inside a garage because the internal combustion engine may produce undesirable gaseous emissions, particularly during start up. In an example embodiment, this issue is addressed by a computer of the vehicle defining a geofence that encompasses the garage. The computer disables the operation of the internal combustion engine when the hybrid electric vehicle is located inside the geofence but allows operation of an electric motor of the hybrid electric vehicle for moving the vehicle out of the geofence. The computer enables operation of the internal combustion engine when the vehicle has moved outside the geofence.

A method of controlling a vehicle includes acquiring biometric data of a user in the vehicle, determining first determination information related to inattention of the user based on the biometric data, acquiring driving related information of the vehicle, determining second determination information related to driving complexity based on the driving related information, and determining whether to provide a feedback function to the user based on the first determination information and the second determination information.

A road shape recognition apparatus is mounted in a vehicle. The road shape recognition apparatus acquires a road image, determines a road shape using the road image, recognizes the road shape based on the road image within a predetermined recognition range, and estimates the road shape outside of the recognition range based on the road shape within the recognition range. The road shape recognition apparatus estimates the road shape outside of the recognition range and within a first distance as a curved line of which a curvature change rate is constant, and the road shape farther than the first distance as a curved line of which a curvature is constant.

An auto driving control system includes: a situation determining unit which recognizes a situation around the vehicle and determines whether automatic traveling is possible; a drive control unit which performs traveling control when the automatic traveling is possible; a state determining unit which determines whether an occupant is able to drive; and an estimation drive control unit configured such that when the automatic traveling is difficult and it is difficult for the occupant to take over the driving, and when it is determined that execution of a function of the automatic traveling is difficult due to a first sensor, the estimation drive control unit executes the function by using information of a second sensor, and performs traveling control of the vehicle, wherein the automatic traveling is continued based on a control instruction of the estimation drive control unit until the occupant becomes able to take over the driving.

A number of variations may include a cable movement detector for determining a movement of a cable. A light source may be directed at the cable. A light detector may receive a reflection of the light source from the cable.

In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.

Apparatuses, systems, and techniques to generate one or more confidence values associated with one or more objects identified by one or more neural networks. In at least one embodiment, one or more confidence values associated with one or more objects identified by one or more neural networks are generated based on, for example, one or more neural network outputs.