A system has a microcontroller with operative control of a set of vehicle lights including an ability to flash all of the set of vehicle signal lights simultaneously as hazard flashers and an ability to strobe all of the set of vehicle signal lights as high conspicuity emergency lighting. Upon receipt of an indication from a vehicle data bus that the vehicle is slowing as a result of a stopping event, the microcontroller determines a speed of the vehicle and strobes the set of vehicle signal lights as high conspicuity emergency lighting if the speed of the vehicle is below a predetermined threshold

Disclosed are a driving guide system and method. The driving guide system includes an input unit configured to receive signal information from a signal controller, a memory in which a driving guide program using the signal information is embedded, and a processor configured to execute the program. When it is not possible to recognize a traffic light using a camera, the processor determines a driving behavior using the signal information and then performs autonomous driving according to the driving behavior or provides a notification to a driver.

A control system for an automated vehicle includes a receiver, an alert-device, and a controller-circuit. The receiver is configured to receive a report of a takeover-event broadcast by another-vehicle at a location, said takeover-event characterized by an other-operator of the other-vehicle engaging a manual-mode of operation of the other-vehicle at the location while the other-vehicle was being operated in an automated-mode. The alert-device is operable to notify a host-operator of a host-vehicle. The controller-circuit is in communication with the receiver and the alert-device. The controller-circuit is configured to, in response to a determination that the host-vehicle is approaching the location, operate the alert-device to notify the host-operator of the host-vehicle about the takeover-event by the other-operator of the other-vehicle at the location.

A road friction is determined for a road location based on a traffic speed determined from respective speeds of each of a plurality of vehicles. A vehicle can be operated based on the determined road friction.

The present disclosure provides a method and a device for controlling a vehicle. The vehicle is in a vehicle queue, the vehicle queue includes n vehicles {vehicle 0, vehicle 1, . . . , vehicle n−1} arranged in order along a traveling direction, the method is performed by a computing device of the vehicle i, i is from 1 to n−1. A vehicle-following subsystem model is established based on the relative speed and the relative vehicle distance error between the vehicle i and a vehicle i−1, and the acceleration of the vehicle i, a state trajectory of the vehicle-following subsystem model on a Δv-ΔR plane is determined, a plurality of division graphs of the vehicle i−1 in a plurality of travelling phases are combined to obtain a switching control graph for the vehicle i, and the travelling phase of the vehicle i is adjusted.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

A method for predicting at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of a surrounding environment of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account in the prediction.

A method for automated parking of a vehicle includes providing image data captured by at least one vision sensor of the vehicle to an electronic control unit (ECU), and providing a parking scene map to the ECU. A free parking space present in the parking scene map is selected as a target parking space, and a parking path from a current vehicle location to the target parking space is formulated. The vehicle is autonomously maneuvered along the parking path towards the target parking space. Responsive to detection of a pedestrian in the target parking space or along the parking path, the vehicle is stopped until the detected pedestrian moves out of the target parking space or out of the parking path. After the detected pedestrian has moved out of the target parking space or out of the parking path, the vehicle continues autonomous maneuvering along the parking path towards the target parking space.

Described is a system for providing an autonomous driving control mechanism in response to a driving obstruction. The system includes a framework for providing a decision process that may learn from surrounding vehicles and traffic flow to determine a suitable responsive action. The system may observe other vehicles and determine a trajectory for the vehicle to follow. The system may rely on a specialized blocking detection and decision components that may provide a set of instructions or rules in order to maneuver around the obstruction. In addition, the system may compare the maneuver with a detour and determine the most suitable route for the vehicle based on an analysis of several factors. Accordingly, the system may continue to provide safe and efficient autonomous control even when encountering a driving obstruction.

Embodiments relate to a method, an apparatus, a computer program, a base station and a vehicle for providing information related to an approaching vehicle. The method comprises Receiving (110) information related to a velocity of a plurality of vehicles. The method further comprises Determining (120) the information related to the approaching vehicle based on the information related to the velocity of the plurality of vehicles. The information related to the approaching vehicle indicates a presence of a vehicle of the plurality of vehicles having a velocity higher than an average velocity of the plurality of vehicles. The method further comprises Providing (130) the information related to the approaching vehicle to at least a subset of vehicles of the plurality of vehicles.