A motion manager configured to request motion of a vehicle according to a kinematic plan on driver assistance of the vehicle to at least one of a plurality of actuators provided in the vehicle includes one or more processors. The one or more processors are configured to arbitrate a plurality of kinematic plans respectively set in a plurality of applications, calculate a motion request to the vehicle based on an arbitration result of the kinematic plans, distribute the motion request to at least one of the actuators, and receive, when an abnormality occurs in at least one of the actuators, information indicating a function in which the abnormality occurs and information for setting an operation of an application corresponding to the abnormality from among the applications.

An adaptive trust calibration based autonomous vehicle may include vehicle systems, a system behavior controller, and a driving automation controller. The system behavior controller may generate a driving automation signal indicative of a desired autonomous driving adaptation. The driving automation controller may control the vehicle systems based on parameters including a desired velocity, current velocity of the autonomous vehicle, desired minimum gap distance between the autonomous vehicle and a detected object, current gap distance gap between the autonomous vehicle and a detected object, relative velocity of the detected object with respect to the autonomous vehicle, desired time headway, desired maximum acceleration, desired braking deceleration, and an exponent. The driving automation controller may receive the driving automation signal and implement the desired autonomous driving adaptation via the vehicle systems by adjusting the parameters based on a type of object associated with the detected object.

A method for determining a distance between a camera positioned on a rear portion of a tow vehicle and a trailer coupler supported by a trailer positioned behind the tow vehicle as the tow vehicle approaches the trailer. The method includes identifying the trailer coupler of the trailer within one or more images of a rearward environment of the tow vehicle. The method also includes receiving sensor data from an inertial measurement unit supported by the tow vehicle. The method includes determining a pixel-wise intensity difference between a current received image from the one or more images and a previously received image from the one or more images. The method includes determining the distance based on the identified trailer coupler, the sensor data, and the pixel-wise intensity difference, the distance includes a longitudinal distance, a lateral distance, and a vertical distance.

A method of processing data for an autonomous vehicle, an electronic device, a storage medium, and an autonomous vehicle are provided. The method includes: acquiring sensor data for the autonomous vehicle, wherein the sensor data includes inertial measurement data, LiDAR data, and visual image data; determining a first constraint factor for the inertial measurement data according to the inertial measurement data and the visual image data; determining a second constraint factor for the LiDAR data according to the inertial measurement data and the LiDAR data; determining a third constraint factor for the visual image data according to the inertial measurement data, the visual image data and the LiDAR data; and processing the sensor data based on the first constraint factor, the second constraint factor and the third constraint factor, so as to obtain positioning data for positioning the autonomous vehicle.

A method for operating an autonomous driving function of a vehicle. The vehicle includes a computer unit and sensors for detecting surroundings data. The computer unit is configured to determine a setpoint trajectory for the vehicle, based on the detected surroundings data. In step a), an actual trajectory, and distances from objects in the surroundings, are detected. In step b), an ascertainment of the quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings. In step c), a control of the quality to a predefined target value takes place by selecting sensors to be used for the autonomous driving function from the plurality of sensors and/or by changing a measuring rate, at which measurements are carried out, of at least one sensor from the plurality of sensors.

The present disclosure provides example autonomous driving apparatuses and computer program products. One example apparatus includes receiving vehicle attribute information and traveling information of a target vehicle from the target vehicle. Layer information of a first road section on which the target vehicle travels is obtained from an autonomous-driving-policy-layer based on the traveling information. A first autonomous driving policy for the target vehicle is obtained based on the layer information of the first road section and the vehicle attribute information of the target vehicle. The first driving policy is sent to the target vehicle.

A method to determine, in real time, a use-life of a steering system includes tracking an attribute signal associated with the steering system. The method further includes, based on a determination that the attribute signal rises above an upper threshold and subsequently falls below a lower threshold, selecting a subset of categories based on a frequency content of the attribute signal. The method further includes selecting a category from the subset of categories based on a peak load occurring in sequence to the attribute signal via a secondary attribute signal. The method further includes incrementing a counter for the selected category. The method further includes computing the use-life based on a ratio of the counter for the selected category and a predetermined count for said selected category.

Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a faster, more reliable, safer, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.

An autonomous driving controller includes a plurality of parallel processors operating on common input data received from the plurality of autonomous driving sensors. Each of the plurality of parallel processors includes communication circuitry, a general processor, a security processor subsystem (SCS), and a safety subsystem (SMS). The communication circuitry supports communications between the plurality of parallel processors, including inter-processor communications between the general processors of the plurality of parallel processors, communications between the SCSs of the plurality of parallel processors using SCS cryptography, and communications between the SMSs of the plurality of parallel processors using SMS cryptography, the SMS cryptography differing from the SCS cryptography. The SCS and/or the SMS may each include dedicated hardware and/or memory to support the communications.

Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.