# Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network
## 8 Conclusions and Future Work  
In the research area of autonomous vehicles and automated driving, the challenge of addressing pedestrian safety will always be a difficult task when implementing new technology. To implement such new technology on the roads used by millions of people everyday requires extensive testing, validation, and verification, all of which can only be completed with adequate data for the task at hand. When testing such technology, there are a multitude of difficulties to consider, such as testing for rare events. It is not feasible, nor safe, to conduct real world testing of scenarios such as those including children or drunken pedestrians, therefore, these tests must be conducted in simulation. However, testing in simulation is not without its own issues. Hard coding a test by an engineer introduces a bias into that test, which limits the fidelity of a test to that very specific scenario, or the very issue of where the relevant scenarios come from. There is a need for CAVs to be able to identify the finer details of a pedestrian, such as intent and other factors that may affect their movement. The research presented in this paper aims at tackling that issue. The Ped-Cross GAN has demonstrated the ability of a generative adversarial network to generate new pedestrian crossing scenarios in the form of human poses. The results highlighted in Section 6 show the encouraging performance in generating new pedestrian crossing scenarios, as well as some short comings that will become the basis of future research. With the introduction of the Pedestrian Scenario dataset, the specific movements, behaviors, and descriptives of pedestrians is captured and extensively labeled. These additional labels take the datasets from simple video data to identify a pedestrian in an image, to an extensive dataset which will show insight into how pedestrians act and behave in a road environment. After disseminating the results, it is clear that there are some issues that need addressing, namely the confidence in every human pose in the dataset being anatomically true. Given the poses generated and displayed in Figure 10, it is clear that the anatomical rules outlined in Section 3.1.2 were not extensive, nor strenuous enough. Following the correction of the dataset, this research will progress to improve on the Ped-Cross GAN, and implement additional labels, as seen in Table 4 and 5. This could drastically improve the use case for the Ped-Cross GAN. The ultimate objective for this research is to be able to train the Ped-Cross GAN to a point where a practitioner will be able to generate new pedestrian crossing scenarios which may have not been captured in video data. They will also be able to generate rare and important crossing events and scenarios, such as those involving elderly people, where the available data is limited. It is envisaged that these generated pose sequences will be used in simulated environments, to test and validate the connected and autonomous vehicles, such that they are able to perform in a safe manner on our roads. This will enable the simulation of rare events, firstly, in a safe controlled environment and secondly, without ever needing to capture the rare event 'in the wild'. All authors have read and agreed to the published version of the manuscript. Conceptualization, S.K. ; Data curation, J.S. ; Formal analysis, J.S., V.P. and M.C. ; Methodology, J.S. and M.C. ; Project administration, V.P., S.K. and A.D.; Resources, V.P., M.C. and S.K. ; Software, M.C. ; Supervision, V.P., M.C., S.K. and A.D.; Validation, J.S. and A.D.; Visualization, J.S. ; Writing--original draft, J.S. ; Writing--review & editing, V.P., S.K. and A.D. This research was funded by a joint initiative called the Centre for Connected and Autonomous Automotive Research (CCAAR). The joint initiative is between Coventry University, the Faculty Research Centre for Future Transport and Cities, and HORIBA MIRA Limited. This study was conducted under the ethical approval of Coventry University, project code: P94776. Not applicable.