Disclosed are apparatuses and methods for controlling driving of a vehicle, the method including obtaining a first image captured by a first ultra-wide-angle lens disposed on a first position in a vehicle, obtaining a second image captured by a second ultra-wide-angle lens disposed on a second position in the vehicle, monitoring a state of a driver and an occupant of the vehicle based on the first image and the second image, detecting an object in a blind spot area based on a matching of the first image and the second image, and generating information for control of the vehicle based on a result of the monitoring and a result of the detecting of the object.

A road surface evaluation apparatus includes a microprocessor configured to perform: acquiring driving information of each of a plurality of vehicles, including a position and an acceleration of each of the plurality of vehicles while traveling, and a map information including road information on a road where the plurality of vehicles travel; evaluating a surface roughness of the road based on accelerations of the plurality of vehicles acquired in the acquiring within a predetermined period and, when a degree of accumulation of the accelerations of the plurality of vehicles acquired within the predetermined period is less than a predetermined value, evaluating the surface roughness by complementing the acceleration of the plurality of vehicles acquired before the predetermined period; and outputting information on the surface roughness evaluated in the evaluating in association with the road information acquired in the acquiring.

A vehicle controller includes a processor configured to detect an object region including another vehicle near a vehicle from each of time series images obtained by a camera mounted on the vehicle; detect a predetermined action taken by the other vehicle, based on a trajectory of the other vehicle estimated from the object region of each image; identify the state of a signal light of the other vehicle, based on characteristics obtained from pixel values of the object region of each image; extract information indicating characteristics of an action of the other vehicle or the state of a signal light at the predetermined action taken by the other vehicle, based on the predetermined action detected in a tracking period and the state of the signal light related to the predetermined action; and predict behavior of the other vehicle, using the extracted information.

A vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.

A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.

A vision system for a motor vehicle comprises an imaging apparatus (11) adapted to capture images (30) from a surrounding of the motor vehicle, and a data processing unit (14) adapted to perform image processing on images (30) captured by said imaging apparatus (11). The data processing unit (14) comprises a traffic sign detector (31) adapted to detect traffic signs in images (30) captured by said imaging apparatus (11) through image processing, a decision section (35) and a traffic sign estimator (36) that is adapted to estimate validity information (37) of one or more traffic signs in an image (30) captured by said imaging apparatus (11).

A method for determining a speed limit on a road segment on which a vehicle is travelling, including steps of obtaining a first speed limit by querying a geospatial database on the basis of a geographical location of the vehicle, of determining a confidence index associated with the first speed limit, of determining a second speed limit by analyzing at least one image obtained from a sensor of the vehicle, of determining a confidence index associated with the second speed limit, of selecting the speed limit associated with the highest confidence index, and of configuring an item of equipment of the vehicle on the basis of the speed limit associated with the highest confidence index.

Automated drive assisting devices, methods, and programs acquire position specifying information for specifying a vehicle position during travel, and acquire a continuity degree that represents a degree to which automated drive can be continued on the basis of the position specifying information acquired during the automated drive. The devices, methods, and programs determine whether the automated drive can be continued on the basis of the acquired continuity degree, and determine vehicle control information for controlling a vehicle such that the continuity degree becomes higher on the basis of the acquired position specifying information in the case where it is determined that the automated drive cannot be continued. The devices, methods, and programs output the determined vehicle control information to a vehicle control device that controls the vehicle.

A system includes an autonomous vehicle and a control device associated with the autonomous vehicle. The control device obtains a plurality of sensor data captured by sensors of the autonomous vehicle. The control device determines a plurality of rainfall levels based on the sensor data. Each rainfall level is captured by a different sensor. the control device determines an aggregated rainfall level in a particular time period by combining the plurality of rainfall levels determined during the particular time period. The control device selects a particular object detection algorithm for detecting objects by at least one sensor. The particular object detection algorithm is configured to filter at least a portion of interference caused by the aggregated rainfall level in the sensor data. The control device causes the particular object detection algorithm to be implemented for the at least one sensor.

An autonomous driving control apparatus includes a sensor device obtaining information around an autonomous vehicle and including a plurality of sensors, a memory storing information about a high definition map around the autonomous vehicle, and a controller classifying the sensors into at least one sensor set based on the information around the autonomous vehicle and the information about the high definition map, using a sensor set classification table, monitoring a computational resource utilization rate and a resource occupancy rate of the memory, calculating a determiner input drop rate and determining whether there is an available resource, using the monitored computational resource utilization rate and the monitored resource occupancy rate, determining whether to additionally allocate at least one determiner using the determiner input drop rate and whether there is the available resource, and changing an autonomous driving determination period.