Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using surfels for vehicle localization. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises a stability measure, wherein the stability measure characterizes a permanence of a surface represented by the surfel; obtaining sensor data for a plurality of locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining a plurality of high-stability surfels from the plurality of surfels in the surfel data; and determining a location in the environment of the first vehicle using the plurality of selected high-stability surfels and the sensor data.

The navigational system comprises a plurality of mobile and stationary telemetry-devices, recording spatial data-artifacts, transmitting forecasted positional phase-changes, density point clustering schema that Maptrac's each transport imechanixm; providing calculated digital navigation directives transmitted to a plurality of devices equipped with pAvics, Obvipro and/or other certified compliant telemetry-devices, attached to and or carried by humans, motorcycles, bicycles, skis, snow boards and or a plurality of transport imechanixms; telematic-data of each transmits collected data-relics through a subnaysys network-topology provided by telemetry mobility, viewable and/or audibly heard on iNavX2 virtual interface and or iNavCom centers and/or a facilities interface.

The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.

A method for the assisted driving of a vehicle. A setpoint trajectory for the vehicle to be followed in a parking facility being ascertained as a function of a type of the vehicle. The ascertained setpoint trajectory is transmitted to the vehicle via a communication network. A digital map of the parking facility is transmitted to the vehicle via the communication network. The vehicle may autonomously drive in the parking facility based on the setpoint trajectory and the digital map. Additionally, the vehicle may be monitored with the aid of a vehicle-external monitoring system while it is driving autonomously in the parking facility. A device for the assisted driving of a vehicle, a method and a device for operating a vehicle, a parking system for vehicles, a vehicle, and a computer program, are also described.

A work machine management apparatus includes: a switchback point setting unit configured to set a plurality of switchback points of a work machine in a work place of a mine; a work point setting unit configured to set at least one work point of the work machine in the work place; a travel track generating unit configured to generate a plurality of target travel tracks along which the work machine travels in the work place based on a position of the at least one work point and a position of each of the plurality of switchback points; and a travel track selecting unit configured to select, among the plurality of target travel tracks, a target travel track along which the work machine travels in the work place.

A method for operating an automatically guided, driverless motor vehicle (1), in particular a passenger car, wherein sensor data detected by environmental sensors, including at least one camera (6) of the motor vehicle (1) is evaluated with respect to objects (17) to be taken into account in trajectory planning, which objects (17) can be classified as an obstacle or not an obstacle by means of at least one classifier evaluating the associated sensor data, wherein in case of an object (17) that cannot be classified or cannot be classified with sufficient certainty as an obstacle or not an obstacle and/or in case of at least one object (17) preventing the further drive of the motor vehicle (1) to a current destination, at least one camera image (18) of the respective object (17) is taken with at least one of the at least one camera (6), transmitted to a portable mobile communication device (11) carried along by a user (12) of the motor vehicle (1), and displayed there; an input, classifying the object (17) as an obstacle or not an obstacle, of a user (12) is accepted as classification information; the classification information is transmitted back to the motor vehicle (1) and is taken into account in the further automatic guidance of the motor vehicle (1).

A surrounding information collection system requests a vehicle to transmit surrounding information, and stores the surrounding information transmitted from the vehicle in response to the request. The surrounding information collection system requests a vehicle to transmit surrounding information, the vehicle acquiring the surrounding information having accuracy greater than a threshold calculated based on accuracy of the stored surrounding information.

The automated lane management assist method, data structure and system receive unprocessed lane-specific limited-access highway information, including lane use and speed limits, from traffic detectors in the roadway or from other sources, process and develop predicted information from these sources and substitute the predicted information for the current information where appropriate, thus improving the timeliness of the information in a form that assists in the selection of driving lanes and target speeds for vehicles, including in partially and fully automated vehicles, and communicate the processed predicted information to the vehicles by suitable means.

In one embodiment, a method for intersection MAP message creation for vehicle communication is provided. The method includes receiving, over a network, map data for an intersection. A processor calculates a reference point location for the intersection from the map data and calculates offset values relative to the reference point location for a plurality of lanes of the intersection from the map data.

A surrounding information collection system requests a vehicle to transmit surrounding information, and stores the surrounding information transmitted from the vehicle in response to the request. The surrounding information collection system requests a vehicle to transmit surrounding information, the vehicle acquiring the surrounding information having accuracy greater than a threshold calculated based on accuracy of the stored surrounding information.