Abstract: A method of determining information related to a road user in an environment of a vehicle includes receiving, from vehicle sensors, a digital image and a Lidar point cloud. The digital image and the Lidar point cloud represent a scene in the environment of the vehicle. The method includes detecting a road user in the scene based on the received digital image and Lidar point cloud. The method includes generating a combined digital representation of the detected road user by combining corresponding image data and Lidar data associated with the detected road user. The method includes determining information related to the detected road user by processing the combined digital representation of the detected road user.

Abstract: 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.

Abstract: The techniques and systems described herein enable a processing unit, a device, or system of a vehicle to notify a driver of hazards. The driver is notified by monitoring a spatial environment surrounding the vehicle and monitoring a driver's attention within the vehicle. At least a first direction from which at least one hazard, on relative approach to said motor vehicle, and is to be noticed by the driver is detected. At least a second direction to which the driver is paying attention is also detected. An alert is generated when the second direction is determined to be different from the first direction. In this way, vehicle safety is improved without annoying passengers or diminishing their wellbeing.

Abstract: A device for detecting vehicle lights in an image, the device is configured to receive an image captured by a camera, wherein the image comprises an array of pixels and each pixel of the image has a respective scene brightness value, compress the image to generate a compressed image, wherein the compressed image comprises an array of pixels and each pixel of the compressed image has a respective grey value, calculate for each grey value in the compressed image a corresponding scene brightness value in the image, and detect vehicle lights in the compressed image by using the calculated scene brightness values.

Abstract: An image processing method includes: determining a candidate track in an image of a road, wherein the candidate track is modelled as a parameterized line or curve corresponding to a candidate lane marking in the image of a road; dividing the candidate track into a plurality of cells, each cell corresponding to a segment of the candidate track; determining at least one marklet for a plurality of said cells, wherein each marklet of a cell corresponds to a line or curve connecting left and right edges of the candidate lane marking; determining at least one local feature of each of said plurality of cells based on characteristics of said marklets; determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells; and determining if the candidate lane marking represents a lane marking based on the at least one global feature.

Abstract: A device for controlling a headlight of a vehicle is configured to receive images consecutively captured by a camera, the images showing an area in front of a vehicle, detect a road in one of the images, divide the road detected in the image in segments, track the segments over time in the images, determine at least one feature related to the brightness for each of the segments, determine from the features related to the brightness of the segments whether preceding and/or oncoming traffic exists, and generate a control signal for controlling a headlight of the vehicle if preceding and/or oncoming traffic exists.

Abstract: A device for detecting vehicle lights in an image, the device is configured to receive an image captured by a camera, wherein the image comprises an array of pixels and each pixel of the image has a respective scene brightness value, compress the image to generate a compressed image, wherein the compressed image comprises an array of pixels and each pixel of the compressed image has a respective grey value, calculate for each grey value in the compressed image a corresponding scene brightness value in the image, and detect vehicle lights in the compressed image by using the calculated scene brightness values.

Abstract: A device for controlling a headlight of a vehicle is configured to receive images consecutively captured by a camera, the images showing an area in front of a vehicle, detect a road in one of the images, divide the road detected in the image in segments, track the segments over time in the images, determine at least one feature related to the brightness for each of the segments, determine from the features related to the brightness of the segments whether preceding and/or oncoming traffic exists, and generate a control signal for controlling a headlight of the vehicle if preceding and/or oncoming traffic exists.

Abstract: An image processing method includes: determining a candidate track in an image of a road, wherein the candidate track is modelled as a parameterized line or curve corresponding to a candidate lane marking in the image of a road; dividing the candidate track into a plurality of cells, each cell corresponding to a segment of the candidate track; determining at least one marklet for a plurality of said cells, wherein each marklet of a cell corresponds to a line or curve connecting left and right edges of the candidate lane marking; determining at least one local feature of each of said plurality of cells based on characteristics of said marklets; determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells; and determining if the candidate lane marking represents a lane marking based on the at least one global feature.