Abstract: Examples are provided for traffic sign detection systems. In one example, a traffic detection system in a vehicle includes an image sensor, a communication system, a processor, and a storage device storing instructions executable by the processor to capture an image of an environment of the vehicle via the image sensor, process the image to detect visual information regarding a traffic sign in the image, the visual information indicating a recognized sign, receive cryptographic data via the communication system from a transmitter associated with the traffic sign, the cryptographic data including a cryptographic representation of a traffic sign signal for controlling the traffic sign, and selectively control one or more vehicle systems of the vehicle based on a cryptographic verification of the recognized sign using the cryptographic data.

Abstract: Techniques are disclosed for the protection and accuracy of system time used in systems, such as automotive systems, from attacks. In some embodiments, a cumulative trustworthiness score is determined for available time sources, other than a real time clock, by adding together trustworthiness scores associated with the available time sources after a system time is initialized to time of the real time clock during booting. The cumulative trustworthiness score is then used to determine an appropriate technique for updating the system time based on time from one of the available time sources, depending on whether the cumulative trustworthiness score is greater than a maximum threshold, between a minimum threshold and the maximum threshold, or less than the minimum threshold.

Abstract: Embodiments are disclosed for reducing unauthorized modifications of object detection systems of motor vehicles. As an example, a method comprises: generating a cryptographic signature for an output of nodes of a fully connected layer of a neural network of an object detection system of a vehicle, the cryptographic signature based in part on a first private key stored in a reply-protected memory block (RPMB), the output at least partially describing a detected object; and, responsive to verifying the cryptographic signature, adjusting vehicle operation based on the detected object. In this way, outputs of the neural network may be verified, so that unauthorized modifications are detected, and system accuracy increases.

Abstract: A system and method to secure lane and traffic management AI models used in an advanced driver assistance system. (ADAS) or in an autonomous vehicle. Only lane and traffic management data recognized from a verifiable source is allowed for testing and training the AI model. All other data is blocked. Controllable parameters of the AI model are encrypted prior to deployment. After deployment, the controllable parameters are decrypted and only signature verified dynamic learning data is applied to the AI model for continuous learning.

Abstract: Examples are provided for object detection systems for vehicles. In one example, a system for a vehicle includes an image sensor, a processor, and a storage device storing instructions executable by the processor to capture, via the image sensor, an image of an environment of the vehicle, detect an object in the image of the environment of the vehicle, determine whether the object is a projected image of the object, and selectively control one or more vehicle systems of the vehicle to perform one or more actions if the object is not the projected image.

Abstract: Embodiments are disclosed for a group security scheme for an autonomous vehicle engaged in a collaborative machine learning approach. As an example, a method comprises: generating, in a vehicle, a digital signature based on a first key and a second key, both of the first key and the second key received from a group manager, and transmitting a message signed with the digital signature to a collaborator, the message including coefficients of a local machine learning model of the vehicle. In this way, an accuracy of the local machine learning model of the vehicle may be increased, while a privacy of the vehicle is increased.

Abstract: Various embodiments include techniques for recovering a cryptographic key in a system that includes secure storage. A computer system implementing these techniques determines that the cryptographic key stored in a first secure storage is corrupted. The computer system retrieves an emergency key from a second secure storage. The computer system establishes communications with a server by using the emergency key.

Abstract: Examples are provided for traffic sign detection systems. In one example, a traffic detection system in a vehicle includes an image sensor, a communication system, a processor, and a storage device storing instructions executable by the processor to capture an image of an environment of the vehicle via the image sensor, process the image to detect visual information regarding a traffic sign in the image, the visual information indicating a recognized sign, receive cryptographic data via the communication system from a transmitter associated with the traffic sign, the cryptographic data including a cryptographic representation of a traffic sign signal for controlling the traffic sign, and selectively control one or more vehicle systems of the vehicle based on a cryptographic verification of the recognized sign using the cryptographic data.

Abstract: Techniques are disclosed for the protection and accuracy of system time used in systems, such as automotive systems, from attacks. In some embodiments, a cumulative trustworthiness score is determined for available time sources, other than a real time clock, by adding together trustworthiness scores associated with the available time sources after a system time is initialized to time of the real time clock during booting. The cumulative trustworthiness score is then used to determine an appropriate technique for updating the system time based on time from one of the available time sources, depending on whether the cumulative trustworthiness score is greater than a maximum threshold, between a minimum threshold and the maximum threshold, or less than the minimum threshold.