Zenseact Ab

Zenuity Ab

Majid Khorsand Vakilzadeh

Magnus Gyllenhammar

Carl Zandén

Junsheng Fu

Zhennan Fei

Mina Alibeigi

Feng Liu

Mina Alibeiginabi

Andreas Valencia Falkovén

Joakim Olsson

Kilian Zwirglmaier

Cagdas Ulas

Andreas Falkovén

An image frames handling system for reconstruction of image frames obtained by an image capturing device of an Automated Driving System, ADS, of a vehicle at an entity. The image system stores in a surrounding state data buffer obtained object-level environmental data indicating states of vehicle surroundings derived from perception data output from an onboard perception system; stores image frames of vehicle surroundings captured by the capturing device in an image data buffer; processes the image data buffer to determine motions of objects in the image frames and respective values of the motions; extracts key image frames from the image data buffer and pixels having motion values exceeding a predeterminable level from non-key image frames of the image data buffer; transfers the extracted key image frames and the extracted pixels; and reconstructs at the entity interpolated image frames at the predeterminable frame rate based on the selected data.

Reconstruction of image frames

The present disclosure relates to a method performed by a buffer resources prioritizing system for storage prioritization in an event buffer configured to continuously collect operational data of an Automated Driving System, ADS, of a vehicle. The buffer resources prioritizing system obtains sensor data of one or more sensors onboard the vehicle. The buffer resources prioritizing system further determines, at least partly based on the sensor data, current ADS-related operational conditions at least comprising states of vehicle surroundings and internal states of the vehicle. Moreover, the buffer resources prioritizing system determines an upcoming scene predicted to evolve from the current operational conditions. Furthermore, the buffer resources prioritizing system deduces based on assessment of the predicted scene, a storage priority score thereof reflecting predicted relevance of freezing event data of the predicted scene in the event buffer. The disclosure also relates to a buffer resources prioritizing system.

Event buffer storage prioritization

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 federated learning. To achieve this, the production vehicle's own worldview is post-processed and used as a reference, towards which the output of the software (SW) or hardware (HW) under development is compared. Through this comparison, a cost function can be calculated and an update of the SW parameters can be locally updated according to this cost function.

Platform for perception system development for automated driving systems

The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. Based on said retrieved data, determining whether there is an anomaly associated with said monitored operation; and taking at least one action of a set of predefined actions if an anomaly is determined.

Method and device for monitoring operations of an automated driving system of a vehicle

The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the 'under-test' path planning module's output is evaluated in closed-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning development-module.

Platform for path planning system development for automated driving system

The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the 'under-test' path planning module's output is evaluated in open-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning module.

A test-optimizing system for supporting shadow mode testing of ADS software included in an ADS-provided vehicle. One or more potential vehicle routes are retrieved. A geographical area covering the one or more potential vehicle routes is obtained and data of crucial locations associated with past vehicle situations identified as critical and/or challenging. Moreover, the test-optimizing system retrieves respective ODD for one or more candidate software respectively adapted to run in the background of the vehicle. ODD-compliant locations for respective candidate software are determined, by identifying locations out of the data of crucial locations lying within respective candidate software's ODD. At least a first test-compliant location along at least a first route out of the one or more potential vehicle routes is determined by identifying for at least a first candidate software, locations out of the ODD-compliant locations situated along the at least first route.

Vehicle software shadow mode testing

Method and server for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A server () provides () a virtual environment () simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (-), human controlled movable virtual objects (-) and at least one virtual AD/ADAS vehicle () operating according to said AD/ADAS functionality. The server () allows devices (-) to remotely connect to the server () and users of said devices (-) to, via user interfaces of the devices (-), control said human controlled movable virtual objects (-), respectively, in the virtual environment (), and thereby cause generation of scenarios that said at least one virtual AD/ADAS vehicle () is subjected to.

Method and server for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system functionality

The present disclosure relates to systems and methods capable of adaptively estimating time-to-take over during ODD exit events, by estimating the recovery time for driver and the action time required to safely handle the situation. In more detail, the proposed system allows for field monitoring for online verification (i.e., in-vehicle verification) of adaptive hand over time, and for facilitated updating of the systems predicting the hand-over time (i.e., Action Time Network and Reaction Time Network) in a decoupled manner by efficient use of data from field monitoring.

System and method for estimating take-over time

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 federated learning. To achieve this, the production vehicle's own worldview is post-processed and used as a reference, towards which the output of the software (SW) or hardware (HW) under development is compared. In case of a large discrepancy between the baseline worldview and perceived worldview by the module-under-test, the data is weakly annotated by the baseline worldview. Such weakly annotated data may subsequently be used to update the SW parameters of the 'perception model' in the module-under-test in each individual vehicle, or to be transmitted to the “back-office” for off-board processing or more accurate annotations.

Platform for perception system development for automated driving system

Method and device for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A device () provides () a virtual environment () simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (-), human controlled movable virtual objects () and at least one virtual AD/ADAS vehicle () operating according to said AD/ADAS functionality. The device allows () a user of the device () to, via user interface (), control said one or more human controlled movable virtual objects () during operation and thereby cause generation of scenarios that the virtual AD/ADAS vehicle () is subjected to.

Method and device for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system functionality

An assessment system for performance evaluation and updating of a PMUD of an ego-vehicle. The assessment system obtains world view data from a perception module configured to generate the world view data based on sensor data obtained from vehicle-mounted sensors; obtains other world view data generated by another perception module; forms a joint world view by matching the world view data the other world view data; and obtains perception data based on a perception model and sensor data obtained from one or more vehicle-mounted sensors. The assessment system further matches the perception data to the formed joint world view; evaluates the obtained perception data in reference to the joint world view to determine an estimation deviation in an identified match between the perceptive parameter of the perception data and a corresponding perceptive parameter in the joint world view; and updates parameters of the perception model based on the estimation deviation.

ADS perception development

An apparatus and method performed by a perception comparing system of a vehicle for perception performance evaluation of an ADAS or ADS. The perception comparing system establishes communication with a secondary vehicle determined and/or estimated to be positioned within a potential range of surrounding detecting sensors on-board the vehicle. The system derives perception data from a perception system of the ADAS or ADS. The system receives secondary perception data from a secondary perception system of a secondary ADAS or ADS of the secondary vehicle and determines a discrepancy output based on comparison of at least a portion of the both of perception data and the secondary perception data. The system communicates acknowledgement data when at least a portion of the discrepancy output fulfils discrepancy exceedance criteria and/or when the vehicle is perceivable in the secondary perception data but the secondary vehicle not is locatable in the perception data.

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Mölndal, Sweden

Majid Khorsand Vakilzadeh