Volvo Car Corporation

Zenuity Ab

Polestar Performance Ab

Nasser Mohammadiha

Anders Movert

Peter Hardå

Ghazaleh Panahandeh

Joakim Lin Sorstedt

Andrew Backhouse

Anders Ödblom

Erik Israelsson

Christopher Innocenti

Henrik LINDÈN

A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.

Path prediction for a vehicle

A method for predicting a trajectory of at least one secondary road user for avoiding a collision course with the secondary road user for a host vehicle. The method includes determining the present location for the host vehicle, retrieving a plurality of modelled clusters of trajectories for a present traffic situation, and detecting the position and speed of the at least one secondary road user. The method also includes predicting at least one feasible trajectory for the at least one secondary road user based on the position and the speed of the at least one secondary road user to the plurality of modelled clusters of trajectories and selecting at least one feasible trajectory of the feasible trajectories for each secondary road user based on a selection criterion. At least one action is performed based on the selected at least one feasible trajectory.

System and method for avoiding a collision course

A method for utilizing a trip history of a vehicle during a trip from an original position to a destination includes: (a) determining the original position; (b) comparing the original position to a mapping database covering the trip; (c) determining a road segment of the mapping database associated to the original position; (d) determining a current position during the trip; (e) comparing the current position to the mapping database; (f) determining a road segment of the mapping database associated to the current position; (g) setting the road segment as a link of the trip; (h) repeating (e)-(g) until the destination is reached; (i) determining the destination; (j) comparing the destination to the mapping database; (k) determining a road segment of the mapping database associated to the destination; and (l) representing the trip as connected links between the original position and destination, each link corresponding to a road segment.

Method and system for utilizing a trip history to predict a destination

A system and method are described for predicting road friction using data from a fleet of connected vehicles. Each fleet vehicle includes a communication arrangement for reporting, to a back end system, floating car data sets, including a position of the vehicle, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position. The data sets are collected and aggregated in a central database, over a predetermined time period. A neural network computer is trained and validated, using aggregated data sets, to create a model to predict future road friction for a specific road network position or segment associated with that specific position. Once trained and validated, the neural network computer, upon receiving the same type of new, up to date, input data, uses the model to predict future road friction for that specific position or segment of the road network.

Arrangement and method for predicting road friction within a road network

A method for providing an alert signal to a control unit of a vehicle for controlling driver intervention. The method comprises determining a set of present driving behavior data indicative of a present driving behavior in a present driving situation and retrieving a driving model indicative of expected driving behavior for the present driving situation. Further, a plurality of expected near future paths for the vehicle are predicted and an actual path is additionally determined. The set of present driving behavior data is mapped with the driving model. When a predetermined degree of deviation in the set of present driving behavior data compared to the driving model is found and the actual path deviates from the predicted expected paths, the alert signal is provided.

Driving intervention in vehicles

A method and a system for predicting a near future path and an associated output control signal for a vehicle. Prediction sensor data, vehicle driving data, and road data are collected. An input control signal indicative of an intended driving action is received. The sensor data and the vehicle driving data are pre-processed to provide a set of object data comprising a time series of previous positions of a respective object relative the vehicle, a time series of the previous headings of the object, and a time series of previous velocities of the object. The object data, the road data, the vehicle driving data, the control signal, and the sensor data are processed in a deep neural network. Based on the processing in the deep neural network, a predicted path output and an output control signal are provided.

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Gothenburg, Sweden

Nasser Mohammadiha