A system and method for automated vehicle control includes referencing map based attributes relevant to an ego vehicles GPS coordinates, establishing control points based upon the map based attributes, and controlling the ego vehicle to the control points with at least one of a steering system, braking system, and powertrain system.

Provided are methods and devices for loss evaluation to automated driving. The method includes: taking classes or localizations of observations as tasks of an automated driving model; correcting loss of each of the observations based on real-world scenarios in driving practice. In the present disclosure, the evaluation of algorithms in automated driving can be set with true realistic value in real world scenario; and rectify the misalignment from using of generic evaluation methods to algorithms used in automated driving scenarios.

A moving range of an object is estimated on the basis of image information. An information processing apparatus includes an input unit that inputs an image, a region estimation unit that estimates a region of an object contained in the image, a moving history information acquisition unit that acquires information associated with a moving history of the object, a contact region determination unit that determines a contact region in contact with the object on the basis of an estimation result obtained by the region estimation unit, and a moving range estimation unit that estimates a moving range of the object on the basis of the moving history containing the contact region of the object. The moving range estimation unit estimates the moving range of the object on the basis of the moving history containing the contact region of the object and a moving track of the object.

Embodiments of the disclosure provide methods and systems for predicting a movement trajectory of a pedestrian. The system includes a communication interface configured to receive a map of an area in which the pedestrian is traveling and sensor data acquired associated with the pedestrian. The system includes at least one processor configured to position the pedestrian in the map, and extract pedestrian features from the sensor data. The at least one processor is further configured to identify one or more objects surrounding the pedestrian based on the positioning of the pedestrian, and extract object features of the one or more objects from the sensor data. The at least one processor is also configured to predict the movement trajectory and a movement speed of the pedestrian based on the extracted pedestrian features and object features using a learning model.

Technologies and techniques for the at least the partially automated driving of a motor vehicle. A first application and at least one redundant second application provide output data depending on motor vehicle operating data and/or environmental data. Vehicle driving data for the at least partially automated driving of the motor vehicle are determined depending on the output data. Vehicle operating data from another vehicle are received, and, depending on the vehicle operating data, the at least one redundant second application switches from an active state to a standby state in which a computer instance of a computer unit used by the at least one redundant second application is at least executed at a lower frequency than in the active state.

A vehicle control device includes a recognizer configured to recognize a surrounding environment of a vehicle, a setter configured to set a first risk area in a surrounding area of the vehicle on the basis of a recognition result of the recognizer, and a controller configured to control at least one of a speed and steering of the vehicle. The setter sets the first risk area so that the first risk area includes an area between the moving object and a first end of a crosswalk where the moving object is scheduled to arrive in the crosswalk when the moving object is entering the crosswalk which is provided in front of the vehicle and where the vehicle is scheduled to pass on the basis of the recognition result of the recognizer. The controller prevents the vehicle from entering the first risk area when a first predetermined condition is satisfied.

The technology involves identifying suitable pickup and drop-off locations based on detected pedestrian walking paths. Mapped areas have specific physical configurations, which may suggest places to pick up or drop off a rider (or a delivery). However, relying solely on map-based information fails to account for how people actually walk or where a most convenient pickup/drop-off spot is located. A walking path heatmap can be generated based on obtained historical and/or real-time pedestrian-related information, which can be obtained by autonomous vehicles driving in areas of interest. Incorporating heatmap information into the evaluation, the system identifies locations for optimized pickup or drop-off in accordance with where pedestrians would likely go. One aspect involves classifying different objects, for instance identifying one or more objects as people who may be walking versus riding a bicycle. Once classified, information about the paths is used to obtain a the heatmap associated with the walking paths.

An embodiment apparatus for preventing a collision at an intersection includes a communication device configured to receive map information, location information, an image around a vehicle, ultrasonic sensor data, or traffic light information, a right turn prediction device configured to predict whether the vehicle will make a right turn at the intersection, based on the map information, the location information, or the image around the vehicle, a right turn performance determination device configured to determine whether the vehicle is able to make the right turn safely, based on the image around the vehicle, the ultrasonic sensor data, or the traffic light information, in response to a prediction that the vehicle will make the right turn at the intersection, and a controller configured to control the vehicle according to the determination of whether the vehicle is able to make the right turn safely.

Technologies and techniques for operating an assistance system in a motor vehicle with which a driving a shoulder by the motor vehicle is detected by means of at least one detection device in the assistance system, and with which a control signal is generated by means of an electronic computing device in the assistance system for engaging with a functional unit in the motor vehicle. The shoulder that is detected is evaluated in terms of the type of shoulder, and a critical or non-critical driving of the vehicle on the shoulder is determined on the basis of the type of shoulder, and the control signal is generated in the case of a critical driving on the shoulder. The present disclosure also relates to an assistance system.

A method for controlling an acceleration rate of a vehicle includes monitoring a first current speed and a first acceleration rate of the vehicle based on the vehicle moving from a standstill. The method also includes setting an initial target acceleration rate to an adjusted target acceleration rate based on the first acceleration rate satisfying a first acceleration adjustment condition and the first current speed satisfying a second acceleration adjustment condition. The method further includes monitoring a second acceleration rate and a second current acceleration rate of the vehicle based on setting the initial target acceleration rate to the adjusted target acceleration rate. The method still further includes setting the adjusted target acceleration rate to the initial target acceleration rate based on the second acceleration rate satisfying a first target acceleration condition or the second current speed satisfying a second target acceleration condition.