Zenseact Ab

Zenuity Ab

Telefonaktiebolaget Lm Ericsson (publ)

Junsheng Fu

Tony Gustafsson

Majid Khorsand Vakilzadeh

Axel Beauvisage

Andreas Schindler

Pontus Kielén

Mattias Brännström

Han Zhang

Zhennan Fei

Joakim Lin Sörstedt

Markus Hammarsten

Viktor Runemalm

Eduardo Sesma Caselles

Marcus Olsson

Erik Steinmetz

Eiliv Hägg

Theodor Stenhammar

David Bejmer

Rafael Valencia Carreño

Blazej Slusarek

Sheng Xu

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.

Determining a state of a vehicle on a road

A method performed by a vehicle pose assessment system for supporting determining a pose of a vehicle in view of a digital map. The approach provided by the method alleviates finding lane segments of the digital map corresponding to current sensor detections, which in turn may support accurate and/or improved vehicle localization. An apparatus and computer storage medium for supporting determining a pose of a vehicle in view of a digital map are also provided.

Vehicle pose assessment

The present disclosure relates to a method for determining a state of a vehicle on a road portion having two or more lanes. The method comprises obtaining map data associated with the road portion and positioning data indicating a position of the vehicle on the road and a sensor data from a sensor system of the vehicle. The method further comprises initializing a filter per lane of the road portion based on the obtained map data, the obtained positioning data, and the obtained sensor data, wherein each filter indicates an estimated state of the vehicle on the road portion. Then, selecting one of the initialized filters using a trained machine-learning algorithm, configured to use the obtained map data, the positioning data, the sensor data, and each estimated state as indicated by each filter as input and to output a current state of the vehicle on the road portion.

Hypothesis inference for vehicles

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

The present disclosure relates to a method to control a vehicle system. The method includes detecting a deviating vehicle having at least one of a deviating behaviour and a deviating vehicle property by means of a perception system of a first vehicle, wherein the perception system includes at least one sensor device configured to monitor a surrounding environment of the first vehicle; assigning a deviating vehicle classification to the deviating vehicle based on the deviating behaviour and/or deviating vehicle property; determining at least one second vehicle to receive information relating to the deviating vehicle; and transmitting to each determined second vehicle a set of information relating to the deviating vehicle, said set of information including at least one of the deviating vehicle classification and a predetermined instruction to be performed by the determined second vehicle, wherein the predetermined instruction is dependent on the deviating vehicle classification.

Method, system, and vehicle for controlling a vehicle system

The present disclosure relates to an overtaking estimating system for estimation of a minimum overtaking speed of a vehicle. The overtaking estimating system determines in view of a host vehicle a remaining distance of an overtaking lane contiguous to a driving lane of the host vehicle. The system further determines a delta distance between the host vehicle and a preceding vehicle positioned in the host vehicle driving lane. Moreover, the system determines a delta time for the host vehicle to reach the preceding vehicle. The system further determines, based on the delta distance, the delta time and a determined host vehicle speed, a speed of the preceding vehicle. Furthermore, the system determines, based on the remaining distance, the delta distance, the preceding vehicle speed, and an overtaking-affecting parameter, a minimum overtaking speed of the host vehicle for overtaking the preceding vehicle in the remaining distance of the overtaking lane.

Estimation of a minimum overtaking speed of a vehicle

Growing community of inventors

Nödinge, Sweden

Junsheng Fu