Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

An embodiment relates to a method for UE operation related to platooning in a wireless communication system, the method comprising: transmitting a negotiation message including first vehicle function information to one or more UEs corresponding to a platooning member by a UE corresponding to a platooning leader; receiving a negotiation response message including second vehicle function information from at least one UE among the one or more UEs; selecting a new platooning leader on the basis of the negotiation response message by the UE corresponding to the platooning leader; and transmitting, by the UE corresponding to the platooning leader, a notification message including information related to the new platooning leader, wherein the negotiation message is related to a request for changing the platooning leader.

Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and/or automated driving (CA/AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include Dynamic Contextual Road Occupancy Map (DCROM) for Perception aspects for VRU safety. Other embodiments are described and/or claimed.

The present disclosure is directed to a traffic signal apparatus communication system and methods of communicating traffic information to vehicles using same. The traffic signal apparatus communication system includes a traffic signal apparatus for providing a message to a vehicle. The apparatus includes at least one spatially encoded marker, and the vehicle is configured to receive returns of a radar signal from the spatially-encoded marker. At least one controller of the vehicle is configured to determine the message encoded by the spatially-encoded marker based on the returns and to control the vehicle based on the message. The message may include a value indicating a time to a transition of a new state of the traffic signal apparatus, where the new state includes emission of light from one of a first light source, a second light source, or a third light source of the traffic signal apparatus.

A control device included in each vehicle composing a vehicle group predicts a vehicle state of a subject vehicle before a traveling regulation point based on speed information of the subject vehicle, light color information of a traffic signal, and distance information between the subject vehicle and an entrance of the traveling regulation point. Based on the vehicle state, it is determined whether a vehicle of which the vehicle state corresponds to an unenterable state is included in the vehicle group. The unenterable state indicates a state where the subject vehicle cannot enter the traveling regulation point before the traffic signal is changed to the yellow light when its speed is maintained at a current speed. When it is determined that the vehicle in the unenterable state is included in the vehicle group, the subject vehicle is controlled such that it does not enter the traveling regulation point.

A system for communicating a driving mode of an autonomous vehicle (AV) comprises the AV, a control device, and a notification device. The control device defines a threshold region around the AV. The control device receives sensor data from sensors of the AV. The control device detects presence of a vehicle from the sensor data. The control device determines a distance between the vehicle and the AV. The control device determines that the vehicle is within the threshold region based on determining that the distance between the vehicle and the AV is within the threshold region. While the AV is operating in the autonomous mode, the control device triggers the notification device to notify the vehicle that the AV is operating in the autonomous mode, where notifying that the AV is operating in the autonomous mode comprises presenting a visual notification and/or communicating a data message to other vehicles.

A system for granting access to an autonomous vehicle comprises the autonomous vehicle, a control device, and a communication device associated with the autonomous vehicle. The communication device receives a signal from a device associated with a user that indicates the user requests the autonomous vehicle to pull over. The control device pulls the autonomous vehicle over to a side of road traveled by the autonomous vehicle. The control device receives a credential associated with the user. The control device determines whether the credential is verified. In response to determining that the credential is verified, the control device grants the user to access the autonomous vehicle.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

A system includes one or more processors that may determine one or more of an energy drag or a parasitic energy loss for upcoming planned travel of a vehicle along one or more routes based on externality information. The one or more processors may determine the one or more of the energy drag or the parasitic energy loss for each of plural, different route locations along the one or more routes and may change one or more aspects of the upcoming planned travel of the vehicle based on the one or more of energy drag or parasitic energy loss that is determined.

A data acquisition unit to acquire data; an inference unit to infer a first inference result by inputting the data acquired to a first machine learning model that outputs the first inference result by using the data as an input; a similarity calculation unit to calculate a similarity between the data acquired and a second inference result on the basis of the second inference result and the data acquired, the second inference result being inferred by inputting the data acquired to a second machine learning model that outputs the second inference result by using the data as an input; a determination unit to determine whether or not to output the first inference result by comparing the similarity calculated with a threshold for inference result determination; and an output unit to output the first inference result when the determination unit determines to output the first inference result are included.