A method for coordinating vehicles of a vehicle group, including implementing a requested setpoint acceleration, wherein the setpoint acceleration for each vehicle is specified as a function of an emergency braking request triggered manually or automatically, assigning a position in the vehicle group to each vehicle, implementing the emergency braking request, providing, in an advanced emergency braking system (AEBS) cascade, at least a first warning phase for an optical or acoustic warning of a driver of the respective vehicle of the vehicle group, and an emergency braking phase for braking the respective vehicle of the vehicle group as a function of the emergency braking request, at an emergency braking time, and providing a haptic warning phase for at least one vehicle of the vehicle group at a haptic time within a framework of the AEBS cascade.

A method for coordinating vehicles of a vehicle group, including implementing a requested setpoint acceleration, wherein the setpoint acceleration for each vehicle is specified as a function of an emergency braking request triggered manually or automatically, assigning a position in the vehicle group to each vehicle, implementing the emergency braking request, providing, in an advanced emergency braking system (AEBS) cascade, at least a first warning phase for an optical or acoustic warning of a driver of the respective vehicle of the vehicle group, and an emergency braking phase for braking the respective vehicle of the vehicle group as a function of the emergency braking request, at an emergency braking time, and providing a haptic warning phase for at least one vehicle of the vehicle group at a haptic time within a framework of the AEBS cascade.

A vehicle includes a system and method of navigating the vehicle. The system includes a sensor and a processor. The sensor captures an image of a roadway. The processor focuses the sensor at a road segment selected from a plurality of road segments of the roadway using a machine learning program based on a risk of the road segment. The machine learning program is trained to focus the sensor by calculating the risk for each of the plurality of road segments of the roadway based on a hazard probability associated with each road segment and an occupancy probability associated with each road segment, selecting the road segment from the plurality of road segments based on the risk associated with the road segment, and determining a reduction in the risk for a road risk model of the roadway due to selecting the road segment.

Systems and methods are provided to provide a steering indicator to a driver of a vehicle. It is determined whether a first obstacle is in a forward path of the vehicle and whether a second obstacle is present at a side of the vehicle. In response to (a) determining the first obstacle is in the forward path and (b) determining whether the second obstacle is present at the one or more sides of the vehicle, a suggested steering action indicator indicating one or more movements for the vehicle to avoid the first obstacle is provided to a user interface of the vehicle.

Systems and methods are provided to provide a steering indicator to a driver of a vehicle. It is determined whether a first obstacle is in a forward path of the vehicle and whether a second obstacle is present at a side of the vehicle. In response to (a) determining the first obstacle is in the forward path and (b) determining whether the second obstacle is present at the one or more sides of the vehicle, a suggested steering action indicator indicating one or more movements for the vehicle to avoid the first obstacle is provided to a user interface of the vehicle.

This invention provides a system for intuitive human-machine interface. The system includes a visual device, an audio device, a haptic device, and a system of chip. The system of chip receives a data transmitted from one of the following groups including at least one electronic control unit, an advanced driver assistance system, a center informative display, and at least one communication system. It determines a first feedback level to which the data matches and drives the visual device, the audio device, and the haptic device to perform difference feedback operations according to the first feedback level, to provide an intuitive cognition message to a driver.

A system comprising a client device and a quiet vehicle proximity alert device. The quiet vehicle proximity alert device is associated with a vehicle and configured to broadcast a set of data with respect to a status of the vehicle. The client device is configured to issue a first notification on receiving the set of data if one or more data from the set of data is found to match one or more pre-specified conditions. A second notification is issued by the quiet vehicle proximity alert device on receiving a request for the second notification transmitted by the client device. The second notification comprises an acoustic notification and a driver alert notification. The acoustic notification is dynamically adjusted in real-time by the quiet vehicle proximity alert device corresponding to the status of the vehicle and a status of the client device.

A system comprising a client device and a quiet vehicle proximity alert device. The quiet vehicle proximity alert device is associated with a vehicle and configured to broadcast a set of data with respect to a status of the vehicle. The client device is configured to issue a first notification on receiving the set of data if one or more data from the set of data is found to match one or more pre-specified conditions. A second notification is issued by the quiet vehicle proximity alert device on receiving a request for the second notification transmitted by the client device. The second notification comprises an acoustic notification and a driver alert notification. The acoustic notification is dynamically adjusted in real-time by the quiet vehicle proximity alert device corresponding to the status of the vehicle and a status of the client device.

A vehicle includes a sensor provided to obtain first motion information which is motion state information of an object, a navigator provided to obtain location information of the vehicle, and a controller including at least one processor configured to process the location information and the first motion information, wherein the controller is configured to receive an expected motion value of the object based on the location information, compare the expected motion value with the first motion information to determine a difference value therebetween, and conclude the object as a dangerous object when the difference value is greater than or equal to a predetermined value.

A vehicle includes a sensor provided to obtain first motion information which is motion state information of an object, a navigator provided to obtain location information of the vehicle, and a controller including at least one processor configured to process the location information and the first motion information, wherein the controller is configured to receive an expected motion value of the object based on the location information, compare the expected motion value with the first motion information to determine a difference value therebetween, and conclude the object as a dangerous object when the difference value is greater than or equal to a predetermined value.