METHOD AND ARRANGEMENT FOR GENERATING CONTROL COMMANDS FOR AN AUTONOMOUS ROAD VEHICLE
Described herein is a method and arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ). An end-to-end trained neural network system ( 4 ) is arranged to receive an input of raw sensor data ( 5 ) from on-board sensors ( 6 ) of the autonomous road vehicle ( 3 ) as well as object-level data ( 7 ) and tactical information data ( 8 ). The end-to-end trained neural network system ( 4 ) is further arranged to map input data ( 5, 7, 8 ) to control commands ( 10 ) for the autonomous road vehicle ( 3 ) over pre-set time horizons. A safety module ( 9 ) is arranged to receive the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons and perform risk assessment of planned trajectories resulting from the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons. The safety module ( 9 ) is further arranged to validate as safe and output validated control commands ( 2 ) for the autonomous road vehicle ( 3 ).
1 . Method for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ), characterized in that it comprises:
providing ( 16 ) as input data to an end-to-end trained neural network system ( 4 ) raw sensor data ( 5 ) from on-board sensors ( 6 ) of the autonomous road vehicle ( 3 ) as well as object-level data ( 7 ) and tactical information data ( 8 );
mapping ( 17 ), by the end-to-end trained neural network system ( 4 ), input data ( 5 , 7 , 8 ) to control commands ( 10 ) for the autonomous road vehicle ( 3 ) over pre-set time horizons;
subjecting ( 18 ) the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons to a safety module ( 9 ) arranged to perform risk assessment of planned trajectories resulting from the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons;
validating ( 19 ) as safe and outputting ( 20 ) from the safety module ( 9 ) validated control commands ( 2 ) for the autonomous road vehicle ( 3 ).
2 . A method ( 1 ) according to claim 1 , wherein it further comprises adding to the end-to-end trained neural network system ( 4 ) a machine learning component ( 11 ).
3 . A method ( 1 ) according to claim 1 , wherein it further comprises providing as a feedback ( 12 ) to the end-to-end trained neural network system ( 4 ) validated control commands ( 2 ) for the autonomous road vehicle ( 3 ) validated as safe by the safety module ( 9 ).
4 . A method ( 1 ) according to claim 1 , wherein it further comprises providing as raw sensor data ( 5 ) at least one of: image data; speed data and acceleration data, from one or more on-board sensors ( 6 ) of the autonomous road vehicle ( 3 ).
5 . A method ( 1 ) according to claim 1 any one of claims 1 , wherein it further comprises providing as object-level data ( 7 ) at least one of: the position of surrounding objects; lane markings and road conditions.
6 . A method ( 1 ) according to claim 1 , wherein it further comprises providing as tactical information data ( 8 ) at least one of: electronic horizon (map) information, comprising current traffic rules and road geometry, and high-level navigation information.
7 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ),
characterized in that it comprises:
an end-to-end trained neural network system ( 4 ) arranged to receive as input of raw sensor data ( 5 ) from on-board sensors ( 6 ) of the autonomous road vehicle ( 3 ) as well as object-level data ( 7 ) and tactical information data ( 8 );
the end-to-end trained neural network system ( 4 ) further being arranged to map input data ( 5 , 7 , 8 ) to control commands ( 10 ) for the autonomous road vehicle ( 3 ) over pre-set time horizons;
a safety module ( 9 ) arranged to receive the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons and perform risk assessment of planned trajectories resulting from the control commands ( 10 ) for the autonomous road vehicle ( 3 ) over the pre-set time horizons;
the safety module ( 9 ) further being arranged to validate as safe and output validated control commands ( 2 ) for the autonomous road vehicle ( 3 ).
8 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ) according to claim 7 , wherein it further comprises that the end-to-end trained neural network system ( 4 ) further comprises a machine learning component ( 11 ).
9 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ) according to claim 7 , wherein it further is arranged to feedback ( 12 ) to the end-to-end trained neural network system ( 4 ) validated control commands ( 2 ) for the autonomous road vehicle ( 3 ) validated as safe by the safety module ( 9 ).
10 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ) according to claim 7 , wherein it further comprises the end-to-end trained neural network system ( 4 ) being arranged to receive as raw sensor data ( 5 ) at least one of: image data; speed data and acceleration data, from one or more on-board sensors ( 6 ) of the autonomous road vehicle ( 3 ).
11 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ) according to claim 7 , wherein it further comprises the end-to-end trained neural network system ( 4 ) being arranged to receive as object-level data ( 7 ) at least one of: the position of surrounding objects; lane markings and road conditions.
12 . Arrangement ( 1 ) for generating validated control commands ( 2 ) for an autonomous road vehicle ( 3 ) according to claim 7 , wherein it further comprises the end-to-end trained neural network system ( 4 ) being arranged to receive as tactical information data ( 8 ) at least one of: electronic horizon (map) information, comprising current traffic rules and road geometry, and high-level navigation information.
13 . An autonomous road vehicle ( 3 ), characterized in that it comprises an arrangement ( 1 ) for generating validated control commands ( 2 ) according to claim 7 .