Systems and methods navigate a vehicle on a road at least partially covered with snow. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images captured of an environment forward of the vehicle, including an area where snow covers a road on which the vehicle travels, analyze at least one of the plurality of images to identify a first free space boundary on a first side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary.

A real-world vehicle includes multiple data sources that generate sensor data that is spatially-mapped to a real-world region; a data fusion system is configured to fuse or integrate (i) the spatially-mapped sensor data with (ii) virtual data, that has been generated outside of the vehicle or generated independently of the operation of the vehicle, and is spatially-mapped to a virtual world. This enables a fusion of the real and virtual worlds which enables a self-driving car to interact not only with the physical world but also to virtual objects introduced into the path of the car (e.g. by a test or development engineer) to test how well the car and its autonomous driving systems cope with the virtual object.

A vehicle control system includes: an inputter that accepts an occupant's operation of a host vehicle; an autonomous driving controller that executes an autonomous driving mode for controlling steering and acceleration/deceleration of the host vehicle in a case where a predetermined operation is accepted by the inputter; and a mode controller that prohibits the autonomous driving mode from being started by the autonomous driving controller in a case where the host vehicle has reached a predetermined section including at least one of a divergence point, a merging point, and a destination.

The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.

A computer implemented method for determining weights for an attention based trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.

The invention relates to an automated driving control system, including: a data exchange unit configured to obtain video data streams and distribute the video data streams to at least one computing unit; the at least one computing unit configured to compute perception result data based on the video data streams; a sensor fusion unit configured to fuse the perception result data and sensor data, to obtain fusion result data; and a planning control unit configured to generate a driving control instruction based on the fusion result data, where the planning control unit or the sensor fusion unit is configured to provide a bypass of the data exchange unit. When anomalies occur in any of the units, the automated driving control system may provide a corresponding bypass to keep an automated driving function going.

A data processing method, apparatus, chip system, and medium are provided. The method may be applied to the field of autonomous driving or intelligent driving. The method includes: obtaining first abstract data from first raw data by using a first recognition algorithm, where the first abstract data includes attribute description data of a first target; receiving first feedback data, where the first feedback data includes attribute description data of a second target; and optimizing the first recognition algorithm based on the first feedback data, where the first raw data is measurement data of a scenario, and the first target and the second target are targets in the scenario.

A method for operating an advanced driver assistance systems (ADAS) includes determining a distance between an object and a vehicle, selecting a path estimation methodology from multiple path estimation methodologies based at least in part on a distance between the vehicle and the object, and activating at least one ADAS action in response to determining that the object intersects the estimated path.

The present disclosure provides a spatial parking place detection method and device, a storage medium and a program product, which relate to the field of data processing and, in particular, to the fields of computer vision, autonomous parking and autonomous driving. A specific implementation lies in: acquiring ultrasonic data around a vehicle collected by an ultrasonic sensor on the vehicle, and image data around the vehicle collected by an image collection apparatus; determining a first spatial parking place around the vehicle according to the ultrasonic data, and determining a second spatial parking place around the vehicle according to the image data; fusing the first spatial parking place and the second spatial parking place that are located at an identical position to determine a spatial parking place at that position; and checking the availability of the detected spatial parking place, and determining available spatial parking places of the vehicle.

A vehicle control device includes a recognizer configured to recognize a surrounding situation of a vehicle, a driving controller configured to control the vehicle, and a first receiver configured to receive a switching operation on a driving mode. The driving controller causes the vehicle to travel in any one of a plurality of driving modes including a first mode and a second mode that a task imposed on an occupant is milder than that in the first mode, and performs acceleration/deceleration control so that a vehicle speed becomes a target speed in a state that the first mode is being executed and the second mode is executable and switches the driving mode from the first mode to the second mode when the first receiver has received an operation for switching the driving mode to the second mode in a state that the speed has become the target speed.