A method is for training an object detector configured to detect objects in sensor data of a sensor. The method includes providing first sensor data of the sensor, providing an object representation assigned to the first sensor data, and transmitting the object representation to a sensor model. The method further includes imaging object representations onto the first sensor data of the sensor with the sensor model, assigning the object representation to second sensor data with the sensor model, and training the object detector based on the second sensor data.

Disclosed are devices, systems and methods related to direct and indirect methods for detecting wind speed and direction during driving. An example method may include estimating, by a processor of a vehicle controller, a speed and a direction of wind movement near the vehicle based on a first sensor output from a wind sensor, or a second sensor output from a non-wind sensor, or a combination of the first sensor output and the second sensor output, wherein a primary purpose of the wind sensor is wind detection, and a primary purpose of the non-wind sensor is different from wind detection, and generating a control output indicative of a vehicle disturbance force resulting from the wind based on the estimated speed and direction of the wind movement.

Maneuver assisting system and method are provided. The system includes at least one processor configured to process operations of the system; one or more memories for storing instructions; a communication unit for communicating between components of the system, and between the system and the vehicle and/or a user equipment; a Surrounding View Monitoring (SVM) unit comprising a plurality of sensors for providing a plurality of vehicle-related information; a Human-Machine Interface (HMI) configured for a driver of the vehicle to interact with the system; a display unit configured to display the HMI; a motion planning unit configured to generate a trajectory for the vehicle to follow; and a motion control unit configured to control the automated maneuvers of the vehicle. In particular, the HMI is configured to be implemented in the user equipment in order for the driver to remotely operate the system using the user equipment.

A robotic cart platform with a navigation and movement system that integrates into a conventional utility cart to provide both manual and autonomous modes of operation. The platform includes a drive unit with drive wheels replacing the front wheels of the cart. The drive unit has motors, encoders, a processor and a microcontroller. The system has a work environment mapping sensor and a cabled array of proximity and weight sensors, lights, control panel, battery and on/off, “GO” and emergency stop buttons secured throughout the cart. The encoders obtain drive shaft rotation data that the microcontroller periodically sends to the processor. When in autonomous mode, the system provides navigation, movement and location tracking with or without wireless connection to a server. Stored destinations are set using its location tracking to autonomously navigate the cart. When in manual mode, battery power is off, and back-up power is supplied to the encoders and microcontroller, which continue to obtain shaft rotation data. When in autonomous mode, the shaft rotation data obtained during manual mode is used to determine the present cart location.

Systems and methods of electrically heating catalyst (EHC) driver notification are provided. With the goal of increasing driver cooperation in reducing emissions, EHC driver notification systems notify the driver when the EHC is in an inefficient operation state. This notification is provided to the driver so that the driver may consciously operate the vehicle in a fashion that reduces emissions while the EHC is in the inefficient operation state. EHC driver notifications systems may also restrict operation of the vehicle when the EHC is in an inefficient operation state. However, for safety reasons, these systems provide the driver a function to bypass the restriction as needed.

Techniques for a pose component that may determine a pose are described herein. A pose may refer to the inertial pose or position of a vehicle which may be updated in real-time or near real-time. For example, the techniques may include receiving a plurality of input signals at a pose component and monitoring the plurality of input signals. The pose component may determine, based on the monitoring of the plurality of input signals, a particular pose update algorithm of a plurality of pose update algorithms for determining the pose and determine, using the particular pose update algorithm, the pose based the plurality of input signals and IMU measurements associated with a primary IMU.

The present disclosure relates to a driver controlling system for a vehicle, a vehicle comprising such a driver controlling system, a driver controlling method for a vehicle and a computer program element for such a driver controlling system.

The driver controlling system comprises a propulsive actuator unit, a propulsive sensor unit and a control unit. The control unit is configured to prompt the propulsive actuator unit to apply at least one driving parameter in an automated driving mode of the vehicle. The at least one driving parameter is based on a driving preference of a driver. The control unit is further configured to modify the at least one driving parameter of the automated driving mode by a defined rate of deviation for causing a reaction of the driver. The propulsive sensor unit is configured to generate driver engagement data based on the reaction of the driver to the at least one modified driving parameter. The rate of deviation for modifying at least one driving parameter may be varied and/or the at least one driving parameter may be incrementally modified from a prior modified driving parameter by the defined rate of deviation, in case of insufficient reaction of the user.

A method and device for operating an automated vehicle at a traffic intersection. The method includes: acquiring an environment of the automated vehicle including the traffic intersection; preparing an environment map based on the acquired environment, the environment map including a subdivision into grid cells, each including occupancy probabilities and velocity distributions; acquiring an updated environment of the automated vehicle; adapting the occupancy probabilities and the velocity distributions for each grid cell; determining the occupancy probabilities and velocity distributions of an expected environment in a next time step; repeatedly executing the steps until a final occupancy probability and a final velocity distribution is determined for each grid cell according to predefined criteria; determining a driving strategy for the automated vehicle as a function of the final occupancy probabilities and the final velocity distributions; and operating the automated vehicle as a function of the driving strategy.

Disclosed are an autonomous driving apparatus and method for an ego vehicle that autonomously travels. The autonomous driving apparatus includes a first sensor to detect a nearby vehicle nearby an ego vehicle, a memory to store map information, and a processor including a driving trajectory generator to generate a first driving trajectory of the ego vehicle and a second driving trajectory of the nearby vehicle based on the map information stored in the memory and a control processor configured to control autonomous driving of the ego vehicle based on the first and second driving trajectories generated by the driving trajectory generator.

An autonomous driving control apparatus for a vehicle includes: a processor that determines whether a current driving condition of the vehicle is a limit situation during an autonomous driving control, performs a demand for control authority transition to a user during the autonomous driving control when the current driving condition is the limit situation, and starts a minimum risk maneuver to transmit a signal for disabling reactivation of the autonomous driving control when control authority is not transferred to the user; and a storage to store a set of instructions and driving condition data to be used by the processor. In particular, the processor demands for engagement of an electronic parking brake device when the vehicle is stopped after the minimum risk maneuver ends.