## 5 Conclusions  
This manuscript explored the use of Trustworthy Artificial Intelligence (TAI) methods to enhance users' physical and environmental security. It focused on traffic and pedestrian safety systems, detecting and predicting behavior, and Intrusion-Detection Systems for IoT systems. The study highlighted TAI's safety and robustness dimensions, considering users' security in physical and environmental safety. It also explored less-exploited TAI domains like Spiking Neural Networks, neuromorphic accelerators, and Dynamic Vision Sensors. As an indispensable element of the modern world, AI is still evolving, and new AI techniques are appearing. Trustworthiness should be a key feature of such solutions, which make people aware of how much they can trust them. We must constantly verify these methods to check their trustworthiness in many dimensions. We noted the following insights and conclusions while analyzing the described methods and systems selected for this manuscript. When it comes to safety regarding traffic, the analysis of the vehicle's environment is very important. As presented in numerous articles, vehicle detection is more straightforward due to the smaller variety of features of such objects. Systems in this area work very well, resulting in the existence of autonomous vehicles. In the case of pedestrian detection, the problem is more complex. Under all conditions, an ideal system to recognize them can improve their safety. Several proposals have been made for such systems, which achieve good results, but it is still worth creating an infallible system. The challenge for neural networks is the perfect detection of objects, preserving high resilience to attacks. Further research should focus on improving current solutions to enhance security. Increasing city safety can also be realized by implementing a \"smart city\" [174; 175]. We think the cooperation of multiple neural networks at different levels can greatly improve our security, which will also be an interesting research direction. Fusion, which involves the seamless integration of information from various sensors, such as cameras and LiDARs, shows promising results [176; 177; 178]. These sensors operate using different technologies, and analyzing environmental conditions for each of them poses a challenge. Moreover, this advanced technique allows for a substantial improvement in detection effectiveness, also in challenging environmental conditions, and it enhances precision and decision reliability in favorable conditions. We identified a small gap worth fulfilling as we found only a few papers concerning explainable SNNs. Fulfilling this gap can go along with explainable AI methods on traditional ANNs. However, some SNNs, based on the ToM, are already explicitly designed, which is a step toward explainable SNNs. Some of the mentioned papers point out that SNNs exhibit a natural ability to resist some adversarial attacks to some extent (probably due to their stochastic nature) under the condition that the parameters of a network are tuned. Hence, it is worth further exploring the parameter space of SNNs to develop a framework for easier parameter tuning against adversarial attacks. It is also worth mentioning that we still do not have a stable and strongly neurobiologically inspired framework for SNNs' training or continuous learning. Hence, designing this kind of framework would be appreciated in the field. In the case of environmental security, which is connected with users' activities in the network (for example, as IoT systems users), we observed the extreme need to provide intelligent intrusion- and attack-detection methods. Also, security is highly joined with the safety dimension of TAI. Users use a network during many activities, and many users and devices connect with the network. They can be potential victims of cyber attacks with different sources, courses, and effects. Also, technological development entails the development of cyber attack methods.