Toyota Motor Europe

Eth Zurich

Inria Institut National De Recherche En Informatique Et En Automatique

Institut National Des Sciences Applquees De Lyon

Institut National Des Sciences Appliquees De Lyon

Institut National De Recherche En Informatique Et En Automatique

Nicolas Vignard

Christian Laugier

Jilles Steeve Dibangoye

David Sierra Gonzalez

Luc Van Gool

Dengxin Dai

Ozgur Erkent

Victor Romero Cano

Alejandro Dizan Vasquez Govea

Vaishakh Patil

Victor Romero-Cano

Christian Wof

A depth maps prediction system comprising a neural network () configured to receive images (I) of a scene at successive time steps (t−1, t, t+1, . . . ) and comprising three sub-networks: an encoder (), a ConvLSTM () and a decoder (). The neural network () is configured so that at each time step: a) the encoder sub-network () processes an image (I) and outputs a low resolution initial image representation (X); b) the CONVLSTM sub-network () processes the initial image representation (X), values for a previous time step (t−1) of an internal state (C(t−1)) and of an LSTM hidden variable data (H(t−1)) of the ConvLSTM sub-network, and outputs updated values of the internal state (C(t)) and of the LSTM hidden variable data (H(t)); andc) the decoder sub-network () inputs the LSTM output data (LOD) and generates a predicted dense depth map (D″) for the inputted image (I).

Depth maps prediction system and training method for such a system

An electronic device for a vehicle for augmenting image data of a passive optical sensor. The electronic device is configured to: receive first image data of a passive optical sensor, the first image data comprising a plurality of pixels in an image plane, receive second data of an active sensor, the passive optical sensor and the active sensor sensing the same scene outside the vehicle, the active sensor including a plurality of scan areas distributed over the scene, the second data including measurement values at returned scan areas, identify at least one cluster based on the distribution of returned scan areas, project the cluster onto the image plane, identify pixels which match with the projected cluster in the image plane, and associate the identified pixels with second data of the matching cluster. Further relates to a system and a method.

Electronic device, system and method for augmenting image data of a passive optical sensor

An electronic device for determining a semantic grid of an environment of a vehicle is provided. The electronic device is configured to: receive first image data of an optical sensor, the first image data comprising a 2D image of the environment, perform a semantic segmentation of the 2D image and project the resulting semantic image into at least one predetermined semantic plane, receive an occupancy grid representing an allocentric bird eye's view of the environment. The control device further includes a neural network configured to determine a semantic grid by fusing the occupancy grid with the at least one predetermined semantic plane.

Electronic device, system and method for determining a semantic grid of an environment of a vehicle

A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set detects a set of road users and, for each road user, a current state including a current speed and a current position. The data storage device includes a finite plurality of behavioral models. The data processor assigns a behavioral model to each road user, probabilistically estimates, for each road user, a belief state comprising a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position, according to the behavioral model assigned to each road user, and determines a risk of collision of the road vehicle with a road user, based on the probabilistically estimated future state of each road user. The output device outputs a driver warning signal or executes an avoidance action if the risk of collision exceeds a predetermined threshold.

Driving assistance method and system

A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set observes apparent states of each road user of the plurality of road users at successive time steps, the data processor assigns a behavioral model stored in the data storage device, the data processor calculating a new maneuver distribution that is a probability distribution over a finite plurality of alternative maneuvers and a new state distribution that is a probability distribution of possible states for each alternative maneuver of the finite plurality of alternative maneuvers. The data processor determines a risk of collision of the road vehicle with another road user, based on the new maneuver and state distributions of the target road user, and outputs one or more of a driver warning signal and executes an avoidance action if the risk of collision exceeds a predetermined threshold.

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Brussels, Belgium

Nicolas Vignard