In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

A vehicle includes a charging port for connection to a charging cable capable of delivering electricity to the vehicle, and an ultra-wide band (UWB) transceiver module. The UWB transceiver module includes a master node and at least three antenna nodes. The at least three antenna nodes are deployed at correspondingly diverse locations in the vehicle at fixed distances from the charging port. The master node is configured to determine a position of an UWB antenna or tag external to the vehicle relative to the locations of the at least three antenna nodes and the charging port.

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

A parking management system includes a guide robot disposed in a parking space provided with a plurality of parking areas, the guide robot being configured to be matched with a vehicle entering the parking space and to be driven ahead of the matched vehicle to guide the matched vehicle to an allocated parking area, and a management server configured to recognize the vehicle entering the parking space, to match the recognized vehicle with the guide robot, to monitor the parking space to identify a parking status of each of the parking areas, to allocate a parking area to the matched vehicle based on the identified parking status, and to control the guide robot to be driven to the allocated parking area.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

Systems and methods for determining precise pick-up locations for passengers who have requested autonomous vehicle rides. In particular, systems and methods are provided for using wireless signals to determine user location. In some examples, wireless ranging technology, such as Ultra Wide Band (UWB), is used to determine the user location. Wireless transceivers are used to determine a mobile device's range, and range information from multiple transceivers is used to determine the mobile's device's position. In some examples, triangulation is used to determine user location, such as triangulation between one or more wireless transceivers and the mobile device. In various examples, wireless transceivers are installed on autonomous vehicles, and in some examples, wireless transceivers are installed in various static locations (e.g., on buildings, lamp posts, or other structures.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

Technologies disclosed relate to a remote intervention system for the operation of a vehicle, which can be an autonomous vehicle, a vehicle that includes driver assist features, a vehicle used for ride sharing services or the like. The system includes a vehicle sending a request for remote intervention to a remote operator when the operation of the vehicle is suspended. The request for remote intervention can include a request for object identification or a request for decision confirmation. The vehicle can update vehicle operation based in part on vehicle-based sensor data and a response to the remote intervention request from the remote operator. The remote operator can be a human operator or an AI operator.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.

A method for monitoring the environment of a vehicle includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is approaching the vehicle, how many people approach the vehicle may also be recorded. The method includes evaluating physical measurement data obtained from the environment of the vehicle to determine whether at least one person is moving away from the vehicle and, if appropriate, the number of people that are moving away from the vehicle is also recorded. The method further includes carrying out a check as to whether the number of people that have moved away from the vehicle corresponds to the number of people that have previously approached the vehicle. In response to the check resulting in a difference, it is determined that the vehicle is in an unsafe state.