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Autonomous vehicles are waiting to address the global automotive mobility challenges through an intelligent smart transportation system, which includes advanced sensor-actuator configurations to control, navigate, and drive the vehicles. Multi-sensor data fusion from the key sensors such as camera, radar, and lidar is used to achieve the environmental perception for autonomous vehicles by capturing the various attributes of the environment. Cameras are the dominant sensors to achieve the perception by providing vision capability to vehicles. The direct interface of the cameras with the dynamic driving environment carries numerous attack surfaces on the camera. Blinding attacks on the cameras are one of the critical attacks with an intention to blind the cameras either fully or partially by projecting light into the cameras to hide the objects which results in failure in object detection.

Modern vehicles are increasingly equipped with multiple advanced on-board sensors and keep generating large volumes of data. Along with the recent advances in a wide range of Machine Learning (ML) algorithms, the vehicular data are being analyzed intelligently to enable users to be better informed and make safer, more coordinated, and smarter use of transport networks. The success of ML model relies on the availability of large set of relevant data so that the underlying model can be trained better. However, it is not possible for a ML model to fetch the complete set of data from a single vehicle, thus, the collaboration of other vehicles are desired in sharing their local model and collaboratively training the model. Collaborative machine learning (CML) mechanism can improve the intelligence of the ML models in different vehicles by transferring the learned knowledge from the local ML model of one vehicle to another across the distributed network.