An autonomous vehicle includes a control system, an array of sensors, processing logic, and a switch. The processing logic generates operation instructions based on sensor data and the control system controls the autonomous vehicle based on the operation instructions. The array of sensors generate the sensor data that is related to objects in an external environment. The switch is coupled between the sensors and the processing logic to buffer the processing logic from the sensor data. The switch is further coupled between the processing logic and the control system to provide the operation instructions from the processing logic to the control system. The switch includes a prioritization engine that prioritizes an order of transmission, from the switch to the processing logic, of the first sensor data over the second sensor data based on received vehicle operation data.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

The present disclosure provides a route processing method and apparatus. The solution includes: acquiring an initial traveling route, which includes a plurality of track points, corresponding to a vehicle; determining a vehicle traveling area, which includes an area where the vehicle is located when traveling to the track point, corresponding to each track point; determining at least one target track point in the plurality of track points according to the vehicle traveling area, where a first obstacle exists in the vehicle traveling area; performing updating processing on a position of each target track point in the initial traveling route respectively according to the position of the each target track point and a position of the first obstacle, and obtaining a target traveling route according to the target track point for which the updating processing has been performed; and controlling the vehicle to travel according to the target traveling route.

A driver assistance system according to an embodiment of the present disclosure includes: a radar provided in the vehicle to have an external sensing field for the vehicle and configured to acquire radar data; a memory configured to store a first graph stored in advance; and a processor configured to determine a static target based on the radar data and driving information comprising a driving velocity, generate a second graph based on the determined static target, and correct the driving velocity based on the first graph and the second graph.

In an apparatus for determining a traveling position of an own vehicle that is an autonomous driving vehicle equipped with the apparatus, a judgment section is configured to judge presence or absence of at least one of a travel history of other vehicles regarding the lane in which the own vehicle is traveling and an object that is located in the vicinity of the own vehicle within the lane in which the own vehicle is traveling and should be avoided coming into contact with. The traveling position is a widthwise position of the own vehicle within a lane in which the own vehicle is traveling. A determination section is configured to determine the traveling position using the travel history in response to the judgment section judging that the travel history exists and using position information of the object in response to the judgment section judging that the object exists.

A method for the preparation of an obstacle map, wherein the obstacle map comprises cells, includes assigning each of the cells to segments of an environment of the vehicle, and assigning to each of the cells information as to whether the corresponding segment of the environment is occupied by an obstacle. The method also includes preparing an environment map that comprises the cells, and determining a threshold value specification, where the threshold value specification specifies different threshold values for the cells of the environment map. The threshold value specification is determined depending on a trajectory of the vehicle. An obstacle map is then determined on the basis of the environment map and the threshold value specification.

A driver assistance system for motor vehicles, including at least one sensor for detecting object properties of objects which are located in the surroundings of the motor vehicle; a first interface; an output unit for transmitting the object properties to a user; and a control unit. The sensor transmits the object properties in a form of a first signal to the first interface. The first interface transmits the object properties, received in the form of the first signal, to the control unit in the form of a second signal, the control unit being configured to forward the object properties, received in the form of a second signal, to the output unit and to control the output of the object properties by the output unit.

A method for monitoring a drive-by-wire system of a motor vehicle, including: temporally offset reading in of at least two input values of an input quantity of an operating element of the motor vehicle; ascertaining a change over time or rate of change over time of the input quantity from the at least two read-in input values; determination of a monitored quantity for the motor vehicle operation from the change over time or rate of change over time; selection of a monitoring function on the basis of the monitored quantity; monitoring of the monitored quantity for the ascertained motor vehicle operation by the monitoring function.

A system receives confirmation that a vehicle has accepted automatic control imposition for a drive within a geo-fenced boundary. The system tracks travel of a plurality of vehicles, including the vehicle, within the geo-fenced boundary. The system may determine that the vehicle has a threshold likelihood of encountering at least one of another vehicle or a boundary of the geo-fence at a threshold speed or above and responsive to the determination, impose automatic control on the vehicle, including at least one of controlled braking or speed limiting.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining sensor data indicative of a surrounding environment of the autonomous vehicle, the surrounding environment including one or more occluded sensor zones. The method includes determining that a first occluded sensor zone of the occluded sensor zone(s) is occupied based at least in part on the sensor data. The method includes, in response to determining that the first occluded sensor zone is occupied, controlling the autonomous vehicle to travel clear of the first occluded sensor zone.