Abstract: A vehicle driving system is equipped with an automatic driving control unit that outputs a signal for automatically actuating a plurality of pieces of in-vehicle operational equipment that can be operated by a driver, a manual driving control unit that outputs a signal to the pieces of the in-vehicle operational equipment based on an operational input by the driver, and an interface control unit that is connected to the automatic driving control unit, the manual driving control unit and the pieces of the in-vehicle operational equipment, that receives at least one of the signal output from the automatic driving control unit or the signal output from the manual driving control unit, and that outputs the received signal to the pieces of the in-vehicle operational equipment.

Abstract: An apparatus includes a position circuit structured to monitor a position of an accelerator of a vehicle and a speed circuit structured to monitor a speed of the vehicle. The position corresponds with an associated response of a prime mover of the vehicle. The associated response includes at least one of a torque output and a power output of the prime mover. The apparatus further includes a response management circuit structured to receive an indication regarding the position of the accelerator and the speed of the vehicle; determine that the indication satisfies a remapping condition, the remapping condition including at least one of a creep condition, an obstacle condition, a deceleration condition, and a reverse condition; and dynamically remap the associated response of the prime mover of the vehicle based on the position of the accelerator in response to the indication satisfying the remapping condition.

Abstract: A distributed centralized automatic driving method comprises a sensor processing module, a perception positioning module, a perception target detection module, a decision-making module, a planning module, and a vehicle control module. By clearly defining data domains and a control process, a modular design is performed to implement each functional method, and each module can be deployed to a control unit of the corresponding data domain according to the load of a computing platform. The distributed centralized automatic driving method has different computing requirements for different scenarios. By means of a distributed design, centralized computing is distributed to different computing unit modules, so as to greatly improve the stability, efficiency and parallelism of the method, thereby improving the overall performance of the method.

Abstract: A system and method for providing a comprehensive trajectory planner for a person-following vehicle that includes receiving image data and LiDAR data associated with a surrounding environment of a vehicle. The system and method also include analyzing the image data and detecting the person to be followed that is within an image and analyzing the LiDAR data and detecting an obstacle that is located within a predetermined distance from the vehicle. The system and method further include executing a trajectory planning algorithm based on fused data associated with the detected person and the detected obstacle.

Abstract: A method for determining a risk of instability of a calculation of an angle ? of a steering shaft of a motor vehicle can be employed where a first gear wheel is fixed to the steering shaft and cooperates with a second gear wheel and a third gear wheel, which are smaller than the first gear wheel. The number of teeth of the first gear wheel is n. The number of teeth of the second gear wheel is m. And the number of teeth of the third gear wheel is m+1. The angles ? and ? of the two smaller gear wheels are determined and the angular position ? of the steering shaft is calculated by evaluating the equation ? = m * ? + ( m + 1 ) * ? - ( 2 ? m + 1 ) * k * ? 2 ? n , with ? being an angle of the sensor range and a whole number k given by k = round ? ( ( m + 1 ) * ? - m * ? ? ) , wherein the risk of instability is determined by calculation of a stability margin t according to t = k - ( ( m + 1 ) * ? - m * ? ? ) .

Abstract: Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

Abstract: This disclosure relates to method and system for validating an Autonomous Vehicle (AV) stack. The method may include receiving an Operational Design Domain (ODD) and real-world data for evaluating at least one of an Advanced Driver Assistance System (ADAS) and the AV. The ODD is based on at least one feature of at least one of the ADAS and the AV. For each of a plurality of iterations, the method may further include generating a driving scenario based on the ODD of the AV and the real-world data through a Quality of Ride Experience (QoRE)-aware cognitive engine, plugging and running at least one of the ADAS and the AV algorithm based on the driving scenario, and determining a set of performance metrics corresponding to the at least one feature of at least one of the ADAS and the AV in the driving scenario based on the simulating.

Abstract: A method and a device for operating an automated vehicle. The method includes a step of detecting surroundings data values, a step of determining positions and/or predicted movements of objects in the surroundings of the automated vehicle, a step of carrying out a first comparison of the surroundings data values and/or of the positions and/or of the predicted movements using an external server, a step of determining a driving strategy for the automated vehicle as a function of the positions and/or predicted movements of the objects and as a function of the first comparison, a step of carrying out a second comparison of the driving strategy using the external server, and a step of operating the automated vehicle as a function of the driving strategy and as a function of the second comparison.

Abstract: A method includes receiving data relating to pedestrian activity at one or more locations outside of a crosswalk, analyzing the data, based on the data, identifying at least one location of the one or more locations as a constructive crosswalk, and controlling operation of an autonomous vehicle based on the at least one location of the constructive crosswalk.

Abstract: A method for door or tailgate operation in a transportation vehicle, wherein the transportation vehicle is equipped with an access control device with which determines whether the operating person is present with an authentication element within a proximity area around the transportation vehicle. The device is equipped with at least one communication module for wireless communication with the authentication element, wherein access authorization information is transmitted from the authentication element to the device, the information being checked in the device, and wherein access is granted to the device following verification of the access authorization. It is determined whether the operating person is located in the immediate vicinity of the door or tailgate to be operated, and whether the operating person performs an operating movement in the immediate vicinity for the door or tailgate operation.

Abstract: A method and an apparatus of controlling a driverless vehicle and an electronic device are provided, and relates to the field of artificial intelligence technologies, such as computer vision and self-driving. The method includes: in a case that a stop instruction is received or a driverless vehicle arrives at a preset position, acquiring a first road surface image captured by a camera of the driverless vehicle that is on a same side of a door of the driverless vehicle; acquiring, based on the first road surface image, a target road surface state of road surface on one or two sides of the driverless vehicle that are provided with a door; in a case that the target road surface state is a first road surface state, controlling the driverless vehicle to stop.

Abstract: Systems and methods for using autonomous vehicle assistance for freeing a vehicle from a stuck condition may include: receiving sensor data from a vehicle sensor indicating a condition of the vehicle; determining from the sensor data that the vehicle is in a stuck condition; obtaining a solution for freeing the vehicle from the stuck condition, wherein the technique is a learned solution developed based on collected data related to vehicle operator techniques for extrication; and taking over at least partial control of the vehicle from its operator and applying the learned technique to the vehicle.

Abstract: Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement—for example, a protected left-turn signal or all-way stop signs—at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This disclosure presents a system that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns.

Abstract: A system receives a road network map that corresponds to a road network that is in an environment of an autonomous vehicle. For each of the one or more lane segments, the system identifies one or more conflicting lane segments from the plurality of lane segments, each of which conflicts with the lane segment, and adds conflict data pertaining to a conflict between the lane segment and the one or more conflicting lane segments to a set of conflict data. The system analyzes the conflict data to identify a conflict cluster that is representative of an intersection. The system groups predecessor lane segments and the successor lane segments as inlets or outlets of the intersection, generates an outer geometric boundary of the intersection, generates an inner geometric boundary of the intersection, creates a data representation of the intersection and adds the data representation to the road network map.

Abstract: A driving assistance device includes: a risk degree obtaining portion obtaining a degree of a risk of an event threatening a safety of a driver when an occurrence of the event is predicted; a risk handling control portion controlling an execution of a risk handling measure by a risk handling unit against the event that is predicted; a risk factor obtaining portion obtaining a factor which causes the risk as a risk factor; and a presentation control portion controlling a presentation of information about the risk factor. The presentation control portion presents information indicating a presence of the risk factor when the risk handling control portion controls the risk handling unit not to execute the risk handling measure and the degree of the risk is equal to or higher than a predetermined value.

Abstract: Methods and systems are provided for warming vehicle tires prior to a vehicle launch event. In one example, a method may include, in response to an initiation of a burn out event while locking non-driven wheel brakes, adjusting a spinning of driven wheels based on vehicle performance parameters measured during a previous vehicle launch. In this way, a vehicle controller may control the tire warming to increase tire traction while reducing tire wear.

Abstract: Techniques for compensating for errors in position of a vehicle are discussed herein. In some cases, a discrepancy may exist between a measured state of the vehicle and a desired state as determined by a system of the vehicle. Techniques and methods for a planning architecture of an autonomous vehicle that is able to provide maintain a smooth trajectory as the vehicle follows a planned path or route. In some cases, a planning architecture of the autonomous vehicle may compensate for differences between an estimated state and a planned path without the use of a separate system. In this example process, the planning architecture may include a mission planning system, a decision system, and a tracking system that together output a trajectory for a drive system.

Abstract: A vehicle control system for automated driver-assistance includes a model-based controller that generates a first control signal to alter an operation of a plurality of actuators of a vehicle based on a reference trajectory for the vehicle, and a present state of the vehicle and actuators. The vehicle control system further includes a neural network controller that generates a second control signal to alter the operation of the actuators of the vehicle based on a reference trajectory for the vehicle, and the present state of the vehicle and actuators. The vehicle control system further includes a combination module that combines the first control signal and the second control signal to operate the actuators based on a combined signal.

Abstract: A fleet operation system for an unmanned aerial vehicle (UAV) is provided. The UAV according to an embodiment of the present disclosure includes a plurality of UAVs configured to fly in a fleet according to a determined task plan; and a ground control device for a fleet operation of the plurality of UAVs. The ground control device includes a first communication unit configured to receive flight information comprising a locations from the plurality of UAVs and transmit the task plan and satellite correction information to each of the plurality of UAVs; a second communication unit configured to transmit the satellite correction information to each of the plurality of UAVs; and a central processing unit configured to generate and transmit the task plan of each of the plurality of UAVs to each of the plurality of UAVs through the first communication unit.

Abstract: Testing autonomous vehicle control systems in the real world can be difficult, because creating and re-creating physical scenarios for repeated testing may be impractical. In some implementations, detailed map data and data acquired through driving in a region can be used to identify similar segments of a drivable surface. Simulation scenarios used to test one of the similar segments may be used to test other of the similar segments. The driving data may also be used to generate and/or validate the simulation scenarios, e.g., by re-creating scenarios encountered while driving in a first segment in a simulation scenario for use in the second segment and comparing simulated driving behavior with the driving data.