A computing device is configured to detect inconsistencies on a vehicle that are typically small and difficult to detect using conventional inspection techniques. Particularly, a spotlight illuminates a target area on the vehicle. The color of the spotlight is the same color as the ambient light illuminating the vehicle but is a higher intensity. Additionally, the color of the spotlight and the ambient light is selected based on the type of inconsistencies expected to be detected. Images of the target area are then captured, digitally processed, and stored in memory, such as a database, for example. Based on the processing of these images, a weighting coefficient is computed and used to generate an enhanced contrast feature map. Inconsistencies are then detected based on the enhanced contrast feature map.

This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying out an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collects any one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to carry out an excavation routine. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, and helping prepare a digital terrain model of the site as part of a process for creating the excavation routine.

The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle.

A vehicle analysis environment includes one or more display screens, such as a display screen wall or an array of display screens. While a vehicle is in the vehicle analysis environment, a vehicle analysis system renders and displays one or more vehicle sensor calibration targets and/or one or more simulated events on the one or more display screens. Vehicle sensors of the vehicle capture sensor data while in the vehicle analysis environment. The sensor data depict the vehicle sensor calibration targets and/or the simulated events that are displayed on the one or more display screens. The vehicle can output actions based on the simulated event and/or can calibrate its vehicle sensors based on the vehicle sensor calibration targets.

A travel control device is configured to make an autonomous driving vehicle travel in such a way that the autonomous driving vehicle arrives at a specified position specified by a user who intends to board the autonomous driving vehicle, when the autonomous driving vehicle has reached a predetermined range from the specified position, transmit an information sending request notifying the user terminal that the autonomous driving vehicle has reached a vicinity of the specified position and requesting sending of position identifying information for identifying a position at which the user intends to board the autonomous driving vehicle to the user terminal via a communication circuit, and when receiving the position identifying information via the communication circuit, change a position of the autonomous driving vehicle, based on the position identifying information in such a way that the autonomous driving vehicle comes close to the user.

A notification apparatus comprises a receiving part that receives a designation of a reference position; a specifying part that specifies a current wheel nut at the same position as a reference wheel nut, which is one of a plurality of wheel nuts included in a reference image that is a captured image acquired by the acquiring part in the past, from the captured image acquired by the acquiring part, with respect to the reference position; and a detecting part that detects looseness of the current wheel nut by detecting that the current wheel nut specified by the specifying part has rotated with respect to the reference wheel nut included in the reference image.

Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate.

Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.

Methods and systems for improved positioning accuracy relative to a digital map are disclosed, and which are preferably used for highly and fully automated driving applications, and which may use localisation reference data associated with a digital map. The invention further extends to methods and systems for the generation of localisation reference data associated with a digital map.

A replacement part validation system is presented that includes a scanning system that scans a received part and provides the scan results of the received part. The system also includes a part recognition module that, based on the received scan results of the received part and provides a part identification and a part quality metric. The system also includes a job assignment module that assigns the received part to an outstanding repair job. The system also includes an output generator that outputs the outstanding repair job and the part quality metric. The system also includes a computing processor that, based on the received scanned results, causes the part recognition module to review a database of parts and retrieve a most-likely match. The part quality metric is based on a comparison of the received part to the most-likely match. The computing processor causes the output generator to output the outstanding repair job and part quality metric to a source.