Method for detecting and acting upon detection of an anomalous vehicle scenario executed by a vehicle which includes: i) an evaluation machine comprising a discriminator network, a generator network, and an encoder network, and ii) a controller, the method comprising training steps executed by the evaluation machine: a) storing noise signals and output signals of the generator network in a replay buffer; b) receiving previous generator output signals (Grb) and previous noise signals (Zrb) from the replay buffer; c) updating the discriminator network, the encoder network, and the generator network, such that the output signal of the discriminator network indicates a confidence of the discriminator network to categorize a first category of signals comprising sensor signals from vehicle sensors and output signals of the encoder network, and a second category of signals comprising noise signals and output signals of the generator network, using: - training signals (x) from vehicle sensors of the vehicle and output signals of the encoder network; - the previous generator output signals (Grb) and the previous noise signals (Zrb); - noise signals (z) and an output signals of the generator network; the method further comprising detection steps executed by the evaluation machine: d) generating an anomaly score, indicating how well a current scenario has been covered in previous drives, which have been used for the training of the models for autonomous control of the vehicle, from an output signal of the encoder network, an output signal of the generator network, and/or an output signal of the discriminator network, wherein an input signal to the encoder network includes data from vehicle sensors; wherein generating the anomaly score includes generating a reconstruction loss value and generating a discriminator score value and wherein the reconstruction loss value and the discriminator score value are linearly weighted into the anomaly score; e) comparing the anomaly score with a pre-set threshold; and f) generating a signal indicating the anomalous scenario depending on the comparison, if the anomaly score is above the pre-set threshold; the method further comprising action steps executed by the controller of the vehicle: g) generating a request for driver take-over of the vehicle during closed-loop operation in the case of indication of anomalous scenario; wherein the request for driver take-over indicates to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake, by reducing speed, vibrating steering wheel and/or drifting steering towards the side of the road to indicate that additional attention is needed. i) an evaluation machine comprising a discriminator network, a generator network, and an encoder network, and ii) a controller, a) storing noise signals and output signals of the generator network in a replay buffer; b) receiving previous generator output signals (Grb) and previous noise signals (Zrb) from the replay buffer; c) updating the discriminator network, the encoder network, and the generator network, such that the output signal of the discriminator network indicates a confidence of the discriminator network to categorize a first category of signals comprising sensor signals from vehicle sensors and output signals of the encoder network, and a second category of signals comprising noise signals and output signals of the generator network, using: - training signals (x) from vehicle sensors of the vehicle and output signals of the encoder network; - the previous generator output signals (Grb) and the previous noise signals (Zrb); - noise signals (z) and an output signals of the generator network; the method further comprising detection steps executed by the evaluation machine: - training signals (x) from vehicle sensors of the vehicle and output signals of the encoder network; - the previous generator output signals (Grb) and the previous noise signals (Zrb); - noise signals (z) and an output signals of the generator network; the method further comprising detection steps executed by the evaluation machine: d) generating an anomaly score, indicating how well a current scenario has been covered in previous drives, which have been used for the training of the models for autonomous control of the vehicle, from an output signal of the encoder network, an output signal of the generator network, and/or an output signal of the discriminator network, wherein an input signal to the encoder network includes data from vehicle sensors; wherein generating the anomaly score includes generating a reconstruction loss value and generating a discriminator score value and wherein the reconstruction loss value and the discriminator score value are linearly weighted into the anomaly score; e) comparing the anomaly score with a pre-set threshold; and f) generating a signal indicating the anomalous scenario depending on the comparison, if the anomaly score is above the pre-set threshold; the method further comprising action steps executed by the controller of the vehicle: g) generating a request for driver take-over of the vehicle during closed-loop operation in the case of indication of anomalous scenario; wherein the request for driver take-over indicates to the driver that additional precaution is needed, such as that driver's hands are put on the steering wheel and the driver has readiness to brake, by reducing speed, vibrating steering wheel and/or drifting steering towards the side of the road to indicate that additional attention is needed.