A platooning controller, a vehicle system including the same, and a method thereof are provided. The platooning controller includes a processor that displays each of a plurality of vehicles forming a platoon as a certain vehicle area and arranges and displays vehicle areas of the vehicles on a screen to be partially overlapped with each other. A storage stores information for configuring the screen by the processor.

Systems and methods for system for controlling a traffic grid, the system comprising a traffic grid including a first roadway and a second roadway, the second roadway crossing the first roadway at an intersection; a special transit lane included within at least one of the first roadway and the second roadway, the special transit lane being configured to share both personal vehicular traffic and special vehicular traffic; a detector configured to detect the presence of a special vehicle within a detection zone, which detection zone is formed within the special transit lane in a predetermined area proximate to the intersection; and a signal light proximate to the intersection configured to control traffic traveling through the intersection, the signal light having a controller; wherein the controller controls the signal light to operate in a first mode of operation based, at least in part, on a detection of a special vehicle by the detector within the detection zone.

According to various embodiments, systems, methods, and mediums for operating an autonomous driving vehicles (ADV) are described. The embodiments use a number of machine learning models to extract features individually from audio data and visual data captured by sensors mounted on the ADV, and then to fuse these extracted features to create a concatenated feature vectors. The concatenated feature vector is provided to a multiplayer perceptron (MLP) as input to generate a detection result related to the presence of an emergency vehicle in the surrounding environment. The detection result can be used by the ADV to take appropriate actions to comply with the local traffic rules.

According to various embodiments, systems, methods, and mediums for operating an autonomous driving vehicles (ADV) are described. The embodiments use a number of machine learning models to extract features individually from audio data and visual data captured by sensors mounted on the ADV, and then to fuse these extracted features to create a concatenated feature vectors. The concatenated feature vector is provided to a multiplayer perceptron (MLP) as input to generate a detection result related to the presence of an emergency vehicle in the surrounding environment. The detection result can be used by the ADV to take appropriate actions to comply with the local traffic rules.

Devices, systems, and methods for boat launching and loading detection and management are disclosed herein. A method may include receiving, by a first device, data associated with operation of a vehicle. The method may include determining, based on the data, that the operation of the vehicle includes at least one of launching a boat into water using the vehicle or loading the boat onto the vehicle. The method may include generating map data indicative of a location associated with the operation of the vehicle. The method may include sending the map data to a second device for presentation.

Devices, systems, and methods for boat launching and loading detection and management are disclosed herein. A method may include receiving, by a first device, data associated with operation of a vehicle. The method may include determining, based on the data, that the operation of the vehicle includes at least one of launching a boat into water using the vehicle or loading the boat onto the vehicle. The method may include generating map data indicative of a location associated with the operation of the vehicle. The method may include sending the map data to a second device for presentation.

Aspects of the present disclosure relate to context aware stopping of a vehicle without a driver. As an example, after a passenger has entered the vehicle, the vehicle is maneuvered by one or more processors in an autonomous driving mode towards a destination location along a route. The route is divided into two or more stages. A signal is received by the one or more processors. The signal indicates that the passenger is requesting that the vehicle stop or pull over. In response to the signal, the one or more processors determine a current stage of the route based on a current distance of the vehicle from a pickup location where the passenger entered the vehicle or a current distance of the vehicle from the destination location. The one or more processors then stop the vehicle in accordance with the determined current stage.

The technology includes communicating the current status of a self-driving vehicle to users, such as passengers within the vehicle and other users awaiting pickup. Certain information about the trip and vehicle status is communicated depending on where the passenger is sitting within the vehicle or where the person awaiting pickup is located outside the vehicle. This includes disseminating the “monologue” of a vehicle operating in an autonomous driving mode to a user via an app on the user's device (e.g., mobile phone, tablet or laptop PC, wearable, or other computing device) and/or an in-vehicle user interface. The monologue includes current status information regarding driving decisions and related operations or actions. This alerts the user as to why the vehicle is taking (or not taking) a certain action, which reduces confusion and allows the user to focus on other matters.

An unmanned ground vehicle (UGV) includes one or more motors configured to drive one or more wheels of the UGV, a memory storing instructions, and a processor coupled to the one or more motors and the memory. The processor is configured to execute the instructions to cause the UGV to determine location information of a movable target; calculate a direction and a speed for the unmanned ground vehicle based on the determined location information; and drive the one or more motors to move the unmanned ground vehicle in the calculated direction at the calculated speed to follow the movable target when the movable target moves.

An unmanned ground vehicle (UGV) includes one or more motors configured to drive one or more wheels of the UGV, a memory storing instructions, and a processor coupled to the one or more motors and the memory. The processor is configured to execute the instructions to cause the UGV to determine location information of a movable target; calculate a direction and a speed for the unmanned ground vehicle based on the determined location information; and drive the one or more motors to move the unmanned ground vehicle in the calculated direction at the calculated speed to follow the movable target when the movable target moves.