A high-fidelity CarSim model is used to collect the data for almost 50 maneuvers for two different tractors with different trailer attached to them. For instance, 10 Single Lane Change (SLC) maneuvers are considered in CarSim including 5 tests with E-class SUV and 5 tests with a pick-up truck. Moreover, at each test, the trailer payload and geometry, CG location, and track width, have been changed to collect sufficient data.


Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems.


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.


For detailed information about this dataset and the tools, please go to our website: http://its.acfr.usyd.edu.au/datasets/usyd-campus-dataset/