Abstract: A system for obtaining a prediction an action (at) of a vehicle (V), including a camera for acquiring a sequence of images (Ft) a convolutional neural network visual encoder which obtains a corresponding visual features vector (vt), one or more sensor that obtains a position of the vehicle (st) at the same time step (st), a Recurrent Neural Network configured to receive the visual features vector (vt) and position of the vehicle (st) at the time step (t) and to generate a prediction of the action (at) of the vehicle (V). The system comprising a command conditioned switch configured upon reception of a control command (ci) to select a corresponding branch of the Recurrent Neural Network. The system is configured to operate the selected corresponding branch to process the visual features vector (vt) and position of the vehicle (st) at the time step (t) to obtain the prediction of the action (at) of the vehicle (V).

Abstract: A method for vehicle (V) environment mapping, comprising the operations of: receiving a set of input values from a plurality of sensors, applying temporal fusion processing to the set of input values, resulting in a respective set of occupancy grid maps, applying data fusion processing to the set of occupancy grid maps, resulting in at least one fused occupancy grid map, detecting discrepancies by comparing occupancy grid maps in the set of maps, resulting in a set of detected discrepancies, processing the at least one fused occupancy grid map and outputting a fused occupancy grid map of drivable spaces. The processing operation includes the step of performing an arbitration of conflict in the at least one fused occupancy grid map. The compound fused occupancy grid map of drivable spaces is supplied (IA) to a user circuit, such as a drive assistance interface.

Abstract: A system for obtaining a prediction an action (at) of a vehicle (V), including a camera for acquiring a sequence of images (Ft) a convolutional neural network visual encoder which obtains a corresponding visual features vector (vt), one or more sensor that obtains a position of the vehicle (st) at the same time step (st), a Recurrent Neural Network configured to receive the visual features vector (vt) and position of the vehicle (st) at the time step (t) and to generate a prediction of the action (at) of the vehicle (V). The system comprising a command conditioned switch configured upon reception of a control command (ci) to select a corresponding branch of the Recurrent Neural Network. The system is configured to operate the selected corresponding branch to process the visual features vector (vt) and position of the vehicle (st) at the time step (t) to obtain the prediction of the action (at) of the vehicle (V).

Abstract: A method for vehicle (V) environment mapping, comprising the operations of: receiving a set of input values from a plurality of sensors, applying temporal fusion processing to the set of input values, resulting in a respective set of occupancy grid maps, applying data fusion processing to the set of occupancy grid maps, resulting in at least one fused occupancy grid map, detecting discrepancies by comparing occupancy grid maps in the set of maps, resulting in a set of detected discrepancies, processing the at least one fused occupancy grid map and outputting a fused occupancy grid map of drivable spaces. The processing operation includes the step of performing an arbitration of conflict in the at least one fused occupancy grid map. The compound fused occupancy grid map of drivable spaces is supplied (IA) to a user circuit, such as a drive assistance interface.

Abstract: Path planning method for computing optimal parking maneuvers for road vehicles including the steps of computing a set of value functions of a cost function of parking maneuvers reaching the target set of states as unique viscosity solution of a Hamilton Jacobi Bellman equation, supplying the set of value functions, together with a starting state of the vehicle, as input to the dynamic programming calculation procedure calculating at least the set of vehicle controls. The set of equations modeling the evolution of the state of said road vehicle is a switched system of equations between a first sub-system if the vehicle is in forward motion and a second sub-system if the vehicle is in reverse motion. The cost function takes into account the arrival time a number of direction changes of the road vehicle between forward motion and reverse motion.

Abstract: Path planning method for computing optimal parking maneuvers for road vehicles including the steps of computing a set of value functions of a cost function of parking maneuvers reaching the target set of states as unique viscosity solution of a Hamilton Jacobi Bellman equation, supplying the set of value functions, together with a starting state of the vehicle, as input to the dynamic programming calculation procedure calculating at least the set of vehicle controls. The set of equations modeling the evolution of the state of said road vehicle is a switched system of equations between a first sub-system if the vehicle is in forward motion and a second sub-system if the vehicle is in reverse motion. The cost function takes into account the arrival time a number of direction changes of the road vehicle between forward motion and reverse motion.