# Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles
## 2 Growing Research Areas in Vehicle Navigation and Control  
From this Special Issue on the GNC of autonomous and transport vehicles and from global policy trends, it is possible to identify areas for which research and development interest is growing in vehicle navigation and control, driven by the needs of the industry and by policy decisions in the field of future transport. The articles collected in this book cover air, ground and sea vehicles over the time horizon from 2022 to 2024. The current trends identified in these articles indicate that navigation challenges in cities or challenging environments represent the leading research direction, followed by trajectory optimisation and control challenges. The navigation and trajectory optimisation challenges are mostly focused on sensor fusion, collaborative localisation or navigation in challenging or urbanised environments. Control challenges are often associated with new actuation and control system technologies. Sensor fusion and sensing for specific environments are the focus of a number of contributions. Contribution 1 introduces a machine learning algorithm that consists of a new version of the light gradient boosting machine (GBM) regression algorithm. This algorithm displays improvements as compared to a random forest algorithm, through faster training and more accurate prediction of the vehicle's position during a global navigation satellite system (GNSS) outage, by using the inertial navigation system (INS) in a GNSS/INS implementation. In Contribution 4, multiple positioning systems including GNSS, simultaneous localisation and mapping (SLAM) and INS are used, and error residuals are used for fault detection and correction in an autonomous vehicle. In Contribution 13, issues with poor GNSS signal quality are also handled for application to forestry vehicles. To deal with a similar problem, Contribution 12 uses computer vision based on a YOLOv4 image detection architecture and an nVidia Jetson AX Xavier device with the ability to handle image quality issues and detect landmarks in the Arctic region. Another computer vision-basedapproach is proposed in Contribution 21, using Kalman filtering with dynamically adjusted measurement noise to allow for precise navigation using multiple tags in environments where GNSS signals are not available. Apart from environment perception issues using sensors, the importance of scene understanding is also emphasised in Contribution 16 for autonomous robot navigation in unstructured environments. Multimodal data fusion is another challenge and is the focus of Contribution 17, wherein a generative adversarial network (GAN) model framework to fuse different types of images and data is proposed for autonomous vehicles. Connected autonomous vehicles are at the centre of several contributions to this Special Issue. In Contribution 3, the focus is on the secure platooning of multiple vehicle convoys against denial-of-service jamming attacks that exploit the fact that such convoys rely on wireless communications. To overcome this cybersecurity challenge, a behaviour-based approach is used where sensor inputs are fed to a behaviour manager that is used to arbitrate between possible actions. The behaviour manager leverages layered costmaps with Vector Field Histogram (VFH) path planning. In Contribution 11, the focus is on energy efficiency. In Contribution 7, the focus is on the specific problem of cooperative control at roundabouts through the optimisation of merge-in flows under vehicle safety constraints. The cost function being optimised accounts for the waiting time and the number of vehicles at the roundabout, with improved queue lengths and safety. Collaborative networks of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are another emerging hot topic. Contribution 5 tackles the cooperative localisation problem in a UAV-UGV vehicle network in three dimensions.