A system for controlling a failure of an environment-friendly vehicle is provided to which a highway driving pilot (HDP) system is applied. The system includes a vehicle control unit (VCU) controller that operates a driving motor, an integrated electric booster (IEB) controller that operates IEB for controlling a brake of the environment-friendly vehicle and generate a request to the VCU controller for regenerative braking, and an HDP controller that calculates a required deceleration of the environment-friendly vehicle based on the situation around the environment-friendly vehicle, determined through cognitive control sensors applied to the environment-friendly vehicle. The HDP controller transmits the required deceleration to the IEB controller. At least one of regenerative braking of the driving motor or braking through the brake is performed based on a type of a fault message output by the IEB controller or a failure in communication between the HDP controller and the IEB controller.

A traveling trajectory estimation system obtains traveling record information indicating a past traveling record of a vehicle, and characteristic object position information indicating the installation position of a characteristic object, and performs a vehicle position estimation process to estimate an object vehicle position at an object time, based on the traveling record information and the characteristic object position information. In the vehicle position estimation process, not only a time previous to the object time, but also a time subsequent to the object time, is used as a reference time. The traveling trajectory estimation system sets each of a plurality of successive times as the object time, and performs the vehicle position estimation process, to estimate the object vehicle positions at the respective times, and determines a collection of the estimated object vehicle positions as a traveling trajectory of the vehicle.

A vehicle control apparatus includes a recognition unit configured to recognize a surrounding situation of a host vehicle, a driving control unit configured to control at least one of acceleration and deceleration and steering of the host vehicle based on a recognition result of the recognition unit and to perform driving control of the host vehicle, and a driving request unit configured to execute a driving change request or a driving operation request based on the surrounding situation during execution of the driving control. The driving request unit is configured to output a first request and a second request different from the first request. The driving request unit comprises a first mode in which the first request is output, and the second request is output when the first request is not satisfied, and a second mode in which the second request is output without outputting the first request.

Aspects of the disclosed technology encompass solutions for automatically managing autonomous vehicle (AV) operating and maintenance tasks, such as implementing an alternative operating mode or ordering replacement parts for an AV components. In some aspects, a process of the disclosed technology can include steps for receiving diagnostic data corresponding with an AV component, determining an estimated life cycle of the AV component, and determining whether to generate an action to implement an alternative operating mode or an order request for one or more replacement parts of the AV component, based on the estimated life cycle of the AV component. Systems and machine-readable media are also provided.

A method for checking at least one driving environment sensor of a vehicle is provided. The vehicle is located on a digital map, features of stored stationary objects in an environment of the vehicle, which are expected to be recognized by the driving environment sensor, are identified in the digital map and the environment of the vehicle is sensed using the driving environment sensor. A degradation of the driving environment sensor is deduced if the features to be recognized according to expectations are not recognized by the driving environment sensor or if features actually recognized by the driving environment sensor deviate strongly from the features to be recognized according to expectations.

A method for controlling autonomous driving in a vehicle capable of the autonomous driving may include collecting vehicle travel status information and system status information during the autonomous driving, sensing a failure based on the system status information, identifying normally controllable actuators when sensing the failure, determining a risk degree corresponding to the sensed failure based on the normally controllable actuator information and the vehicle travel status information, determining a safety state based on normally controllable actuator information and the risk degree, and determining a failure safety strategy corresponding to the safety state.

The disclosure relates to a method that models a motor vehicle sensor in a virtual test environment by way of definition. Using a sensor support, a raycast distribution shape, a group of raycast properties, a raycast reflection factor, and a raycast echo, a sensor in reality may be tested in a virtual environment to calibrate the sensor in reality. The sensor support is a virtual sensor support for a virtual sensor model, which forms a three-dimensional or two-dimensional avatar of the sensor in reality, in the virtual test environment. The sensor support has a sensor starting point that is used as an origin for a raycast distribution shape. The method extracts a special application of the sensor in reality in an application case, which is particularly useful for testing scenarios.

The technology relates to partially redundant equipment architectures for vehicles able to operate in an autonomous driving mode. Aspects of the technology employ fallback configurations, such as two or more fallback sensor configurations that provide some minimum amount of field of view (FOV) around the vehicle. For instance, different sensor arrangements are logically associated with different operating domains of the vehicle. Fallback configurations for computing resources and/or power resources are also provided. Each fallback configuration may have different reasons for being triggered, and may result in different types of fallback modes of operation. Triggering conditions may relate, e.g., to a type of failure, fault or other reduction in component capability, the current driving mode, environmental conditions in the vicinity of vehicle or along a planned route, or other factors. Fallback modes may involve altering a previously planned trajectory, altering vehicle speed, and/or altering a destination of the vehicle.

An IMU sensor fault detection method and apparatus for a multiple IMUs and GNSS integrated navigation system is disclosed. The method is based on a decentralized Kalman filter. In a navigation system in which multiple IMU sensors and GNSS sensors are integrated, a fault of an IMU sensor is detected through correlation analysis between fault detection test statistics of each sub-filter consisting of each IMU sensor.

An IMU sensor fault can be detected and meet the navigation continuity probability requirement required by the system to support the operation of high-safety autonomous vehicles. By considering the correlation between the sub-filters, the continuity requirement assigned to each sub-filter is relaxed, and the relaxed continuity requirement has a direct effect on the improvement of the navigation system availability, contributing to the increase of the system availability.

Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.