Systems and methods for improving fuel economy in vehicles such as Class 8 trucks are provided. In some embodiments, signals indicating states of the powertrain are collected and used to generate fuel rate optimization values. Fuel rate optimization values may indicate a difference between optimum fuel flow rates and actual fuel flow rates during a vehicle drive cycle. Recorded fuel rate optimization values may be used to compare different vehicle configurations during testing, and may also be used to evaluate vehicle performance during real-world operation.

An autonomous driving system including: a first system on chip (SoC) configured to control an autonomous driving function of a vehicle and including a first safe parking module configured to control parking of the vehicle according to first failure information; and a second SoC configured to be driven at a higher operating performance than an operating performance of the first SoC, configured to control the autonomous driving function of the vehicle, and including a second safe parking module configured to control the parking according to second failure information provided, wherein the first SoC or the second SoC is configured to be selectively driven according to level information corresponding to an autonomous driving level, and, based on a failure occurring in the first SoC, the second safe parking module is further configured to control the parking or stopping of the vehicle in response to receiving the second failure information.

A motion of a vehicle is controlled according to a sequential compositions of the elementary paths following a transformation of one of a first pattern, a second pattern, and a third pattern. The first pattern defines a forward motion of the vehicle from a first state to a second state while turning left followed by a backward motion of the vehicle from the second state to a third state while turning right, wherein the orientation of the first state is opposite to the orientation of the second state, and wherein the orientation of the first state is equal to the orientation of the second state. The second pattern defines the motion of the vehicle from a fourth state to a fifth state while moving left, wherein the orientation of the fifth state is leftward perpendicular to the orientation of the fourth state. The third pattern defines a forward motion of the vehicle from a sixth state to a seventh state while turning first left and then right followed by a backward motion of the vehicle from the seventh state to an eighth state while turning first right and then left followed by a forward motion of the vehicle from the eight state to a ninth state while turning first left and then right, wherein the orientation of the sixth state equals the orientation of the seventh state and equals the orientation of the eighth state and equals the orientation of the ninth state. The functions representing the patterns are stored in a memory and are used, in response to receiving an initial state and a target state of the vehicle, to determine parameters of the minimum-curvature path. The motion of the vehicle is controlled according to the parameters of the minimum-curvature path.

A computer-implemented method for designing a state controller with stochastic optimization. The method includes receiving a state space model for describing a system to be controlled, wherein the state space model comprises a system matrix, a state vector which contains one or more state variables, an input matrix, and an input variable vector, wherein the input variable vector is based on the state vector and a feedback matrix which describes the state controller, and the one or more state variables are described on the basis of one or more probability distributions. The method further includes describing an optimization problem which includes a cost function which is calculated at least using the system matrix, the feedback matrix, an initial state, and the input matrix, and solving the optimization problem in order to determine the entries of the feedback matrix.

A method for identifying scenarios of interest for development, verification and/or validation of an ADS of vehicle. Obtaining risk map of surrounding environment of vehicle, risk map is formed based on actuation capability of vehicle and location of free-space areas in surrounding environment. The actuation capability comprises uncertainty estimation for actuation capability and location of free-space areas comprises uncertainty estimation for estimated location of free-space areas. Risk map includes risk parameter for each of a plurality of area segments comprised in surrounding environment of vehicle. Determining compounded risk value of ADS based on risk parameters of a set of area segments of risk map. Monitoring scenario trigger by monitoring at least one of determined compounded risk value against compounded risk trigger threshold, a development of risk map over time against a map volatility trigger threshold, and a development of compounded risk value over time against a risk volatility threshold.

A device for operating an apparatus comprising a first controller configured to be controlled by a first control signal, a second controller configured to be controlled by a second control signal, a control unit operatively connected to the first controller and the second controller, wherein the first controller and the second controller are both configured to operate the apparatus.

An ECU (Electronic Control Unit) evaluation apparatus, for use in vehicle design, allocates functions appropriately to respective ECUs of a vehicle. The function allocation can be performed based on user-specified priority aspect(s) in conjunction with stored information concerning the respective functions and information concerning the ECUs, such as installation positions on the vehicle, mechanical and electrical specifications of component parts of ECUs, etc.

A device for operating an apparatus comprising a first controller configured to be controlled by a first control signal, a second controller configured to be controlled by a second control signal, a control unit operatively connected to the first controller and the second controller, wherein the first controller and the second controller are both configured to operate the apparatus.

Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

Disclosed is a hybrid power system including a computing core, a power converter, a driving motor, an engine generator, a charging stand, and a battery pack. The power converter is coupled to the computing core. The driving motor is coupled to the power converter. The engine generator is coupled to the power converter. The charging stand is coupled to the power converter. The battery pack is coupled to the power converter. When inputting a required torque to the computing core and switching to a charging mode, an electric energy source is coupled to the charging stand and provides power to the battery pack through the power converter. The computing core executes an optimal power allocation algorithm.