Zenuity Ab

Zenseact Ab

Joakim Lin Sörstedt

Tony Gustafsson

Andreas Schindler

Jonas Ekmark

Mattias Brännström

Han Zhang

Junsheng Fu

Axel Beauvisage

Mats Nordlund

Pontus Kielén

Eduardo Sesma Caselles

Erik Steinmetz

The present invention relates to methods and systems that utilize the production vehicles to develop new perception features related to new sensor hardware as well as new algorithms for existing sensors by using self-supervised continuous training. To achieve this the production vehicle's own perception output is fused with other sensors in order to generate a bird's eye view of the road scenario over time. The bird's eye view is synchronized with buffered sensor data that was recorded when the road scenario took place and subsequently used to train a new perception model to output the bird's eye view directly.

Platform for perception system development for automated driving system

The present disclosure relates to a method for determining a vehicle pose, predicting a pose (x, y, θ) of vehicle on a map based on sensor data acquired by a vehicle localization system, transforming a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and predicted pose of the vehicle. The transformed set of map road references form a set of polylines in image-frame coordinate system. Identifying a set of corresponding image road reference features in an image acquired by vehicle mounted camera, where each identified road references feature defines a set of measurement coordinates (x, y) in image-frame. Projecting each of identified set of image road reference features onto formed set of polylines in order to obtain a set of projection points.

Vehicle localisation

A method and control system for generating and updating digital maps using a plurality of passages along a road portion by at least one road vehicle is provided. The method comprises obtaining positioning data and sensor data of each passage from the at least one road vehicle. Further, the method comprises forming a sub-map representation of the surrounding environment at each obtained longitudinal position based on the obtained sensor data, and estimating a longitudinal error for each obtained longitudinal position within each segment. Furthermore, the method comprises determining a new plurality of longitudinal positions of each road vehicle for each passage by applying the estimated longitudinal error on each corresponding obtained longitudinal position, and applying the determined new plurality of longitudinal positions on associated sensor data in order to generate a first layer of a map representation of the surrounding environment along the road portion.

Method and system for generating and updating digital maps

A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

Localization using dynamic landmarks

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Gothenburg, Sweden

Joakim Lin Sörstedt