A vehicle driver assist system includes an expert evaluation system to fuse information acquired from various data sources. The data sources can correspond to conditions associated with the vehicle as a unit as well as external elements. The expert evaluation system monitors and evaluates the information from the data sources according to a set of rules by converting each data value into a metric value, determining a weight for each metric, assigning the determined weight to the metric, and generating a weighted metric corresponding to each data value. The expert evaluation system compares each weighted metric (or a linear combination of metrics) against one or more thresholds. The results from the comparison provide an estimation of a likelihood of one or more traffic features occurring.

A method for preserving the state of health of a traction battery of a hybrid motor vehicle includes: a) acquiring a journey to be made via a navigation system installed in the hybrid motor vehicle, b) dividing the journey into successive portions, c) allocating attributes characterising each of the portions, d) determining, for each of the portions, a curve or a map linking every fuel consumption value of the hybrid motor vehicle for the portion to a charge or discharge value of the traction battery, e) determining an optimal point on each curve or map that makes it possible to minimise the ageing of the traction battery over the entire journey and to ensure that the traction battery is completely discharged on completion of the journey, and f) generating an energy management setpoint depending on the coordinates of the optimal points.

A hybrid electric vehicle includes an intelligent vehicle controller, an electric motor, a battery, an internal combustion engine (ICE), and an electrical generator coupled to the ICE configured to provide electricity to the battery and the electric motor. The intelligent vehicle controller receives ICE power level shifting data from the electrical generator, ICE, battery, and electric motor. The intelligent vehicle controller determines a desirable torque and/or a desirable revolutions per minute (RPM) for the ICE based on the received ICE power level shifting data by utilizing an efficiency map that includes fuel efficiency contours and noise, vibration, and/or harshness (NVH) level lines for the hybrid electric vehicle. The intelligent vehicle controller may have first and second vehicle operation modes, and may derive first and a second desirable power levels for the ICE in the first and second operation modes, based on the ICE power level shifting data.

An object is to reduce a current detection error of a current sensor while suppressing upsizing of a power conversion device equipped with the current sensor. A power conversion device includes a power conversion circuit; a conductor to transmit current to the circuit; and a coreless current sensor to detect the current. The coreless current sensor includes: a magnetic field detection portion; and a shield portion facing the magnetic field detection portion. The conductor includes: a first conductor portion that passes through a space between the magnetic field detection portion and shield portion; and a second conductor portion connected to the first conductor portion via a first bent portion, and the first bent portion is formed such that the space between the magnetic field detection portion and shield portion is not disposed in a direction perpendicular to a face of the second conductor portion closest to the shield portion.

A method of predicting actions of one or more actor agent in a scenario is implemented by an ego agent in the scenario. A plurality of agent models are used to generate a set of candidate futures, each candidate future providing an expected action of the actor agent. A weighting function is applied to each candidate future to indicate its relevance in the scenario. A group of candidate futures is selected for each actor agent based on the indicated relevance, wherein the plurality of agent models comprises a first model representing a rational goal directed behaviour inferable from the vehicular scene, and at least one second model representing an alternate behaviour not inferable from the vehicular scene.

Technical solutions are described for providing a driver performance feedback to a driver of a vehicle. An example method includes receiving, by a controller, a first maneuver control from an automated driving system of the vehicle. The method further includes receiving, by the controller, a second maneuver control from the driver. The method further includes, in response to the first maneuver control being different from the second maneuver control, generating a driver notification that is indicative of the first maneuver control from the automated driving system, and operating the vehicle using the second maneuver control.

The present disclosure relates to an apparatus (1) for estimation of a vehicle state. The apparatus (1) includes a controller (21) configured to determine a first estimation of the vehicle state in dependence on at least one first vehicle dynamics parameter. A filter coefficient (F.sub.C) is calculated based on a first vehicle operating parameter. An operating frequency of a first signal filter (35) is set in dependence on the determined filter coefficient (F.sub.C) and the first estimation is filtered to generate a first filtered estimation of the vehicle state. The present disclosure also relates to a vehicle; and to a method of estimating a vehicle state.

A temporal prediction model for semantic intent understanding is described. An agent (e.g., a moving object) in an environment can be detected in sensor data collected from sensors on a vehicle. Computing device(s) associated with the vehicle can determine, based partly on the sensor data, attribute(s) of the agent (e.g., classification, position, velocity, etc.), and can generate, based partly on the attribute(s) and a temporal prediction model, semantic intent(s) of the agent (e.g., crossing a road, staying straight, etc.), which can correspond to candidate trajectory(s) of the agent. The candidate trajectory(s) can be associated with weight(s) representing likelihood(s) that the agent will perform respective intent(s). The computing device(s) can use one (or more) of the candidate trajectory(s) to determine a vehicle trajectory along which a vehicle is to drive.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

A route generation apparatus (13) has: a generating device (132) and a setting device (132). The generating device generates, on the basis of an evaluation score (SC2), a moving route of a movable object (1) that reaches a second position (WP_end) from a first position (WP_start) so as to avoid an interference between the movable object and an obstacle (O). The evaluation score is obtained by executing a weighting process on a distance (D_FL, D_FR, D_RL, D_RR) between the obstacle and specific portions (E_FL, E_FR, E_RL, E_RR) of the movable object on the basis of weighting coefficients (w_FL, w_FR, w_RL, w_RR). The setting device sets at least one weighting coefficient on the basis of a moving condition of the movable object during a period when the movable objects moves on the moving route.