Pcms Holdings, Inc.

Drnc Holdings, Inc.

Mikko Tarkiainen

Ari Virtanen

Matti Kutila

Pertti Peussa

Samian Kaur

Jussi Ronkainen

Jani Mantyjarvi

Some embodiments of a method disclosed herein may include: receiving a predicted driving route, sensor ranges of sensors on an autonomous vehicle (AV), and sensor field-of-view (FOV) data; determining whether minimum sensor visibility requirements are met along the predicted driving route; predicting blind areas along the predicted driving route, wherein the predicted blind areas are determined to have potentially diminished sensor visibility; and displaying an augmented reality (AR) visualization of the blind areas using an AR display device.

Method and system for in-vehicle sensor range and FOV AR visualization

Systems and methods described herein are provided for determining a platoon configuration for a group of vehicles, determining a set of routes connecting two locations, determining for each route segment the platoon configurations supported and the availability of roadside units on the route segment, and selecting a route from the set of routes connecting the two locations. A route may be selected based on the availability of a roadside unit (RSU) to request an extended time period for a green light to enable a length of the platoon to traverse through an intersection prior to the time period ending. Systems and methods described herein may enable a reconfiguration of a platoon to meet a platoon size restriction for a segment of the selected route.

System and method for routing and reorganization of a vehicle platoon in a smart city

Systems and methods described herein determine an optimal lane trajectory and route for an autonomous vehicle under changing road conditions and forecast autonomous car performance along a route. A road's profile is analyzed to determine a safe lateral lane position. In ice or snow, a vehicle may follow ruts made by previous vehicles, even if following such ruts does not place a vehicle in a lane's center. In warmer weather, a vehicle may drive outside the ruts to avoid risks of aquaplaning. Dynamic road conditions and AV support along a route are used to maximize AV use and to forecast AV performance. A vehicle control system calculates sensor limitations and uses these sensor limitations when calculating which route to select.

System and method for optimizing autonomous vehicle capabilities in route planning

Systems and methods related to dynamic map layers for autonomous or semi-autonomous vehicles and allowing real-time (e.g. latency of less than one second) updates utilizing a plurality of available surrounding sensors (e.g., collaborative sensors), including roadside sensors and sensors of other nearby vehicles. When a vehicle detects an obstruction to a field of view of a sensor, the vehicle may request sensor data from other vehicles to fill the gap in sensor data of the vehicle. This allows the vehicle driver or vehicle illumination system to be focused on the obstructed area.

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Tampere, Finland

Mikko Tarkiainen