Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios.