## 3 Physical and Mixed Safety
### Trustworthy and Explainable SNNs
Although they did not use the Dynamic Vision Sensor (DVS) (an explanation is given further in this subsection), their system was inspired by the primary visual cortex (V1), and its neurons respond specifically to differently oriented lines. The system provides robustness for detecting humans mostly occluded with clothing, people wearing helmets, where the rest of the body is not visible, and human detection in rainy weather or in low-light conditions. They also proposed a solution for neuromorphic stereovision processing. Finally, they proved their system performance by achieving a 95% pedestrian detection rate and showing robustness in detecting bike riders against dark and wet conditions. The next year, in 2015, [PERSON] and [PERSON] [90] proposed a system with a 99% successful detection rate for its new use case. Zooming our considerations out from a particular vehicle or agent, [PERSON] et al. [91] utilized the SVM and SNN as two alternatives for predicting crowd movement trajectory, which is critical for many physical safety applications. They simulated crowd movement at the microscopic level (interaction between individual agents). After comparing the two alternatives, it became clear that the SNN performed better than the SVM in predicting crowd behavior. Although the authors assumed that using methodologies developed from SVM could provide better results than the currently obtained, they also stated that it should still be possible to achieve better performance with SNNs in future work. To a certain extent, crowd movement is related to Driving Scene Understanding (DSU), as people's movements can be a crucial part of DSU. DSU aims to understand ongoing on-road driving scenarios [92]. [PERSON], in his thesis [93], focused on DSU, using an SNN (by learning the ANN and converting it to the SNN). To train his 3D-CNN network, he used visual data only from the Honda Research Institute Driving Dataset (HDD) [92]. The best mean result for the goal-oriented action layer was achieved by the SNN using the \"True Max U\" method as a counterpart for the MaxPooling layer. The converted network was deployed on the GPU SNN simulator. The best mean result for the cause-oriented layer--which heads up towards explainable AI--was achieved by using a nonspiking ReLU ANN. He also proposed two new methods for performing a neuromorphic-friendly implementation of the MaxPooling layer. He proved their efficacies on the Intel Loihi neuromorphic chip using the Nengo, NengoDL, and NengoLiohi libraries. [PERSON] et al. [94] remarked that AI systems often interact with multiple agents, which leads to safety risks (physical safety in that context) arising from such interactions. They proposed a solution for AI agents interfacing with others, a use case for the Theory-of-Mind-based SNN (ToM-SNN). Their ToM-SNN was trained using Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) [95; 96]. Their SNN aims to recognize other agents' risky mental states and help them when necessary. In a series of experiments performed in a simple grid world, their agent helped others avoid safety risks (rescue behavior chosen). It recognizes others' behavior policies and behaves differently for different agents. Hence, the mentioned paper can be crucial for DSU systems and AI development towards TAIHowever, the authors stated that much work is needed to scale their ToM-SNN model. They targeted creating a model inspired by the mirror neuron system, the model that understands others' actions and beyond that. [PERSON]'s and [PERSON]'s solutions, mentioned above, could be beneficial also for enhancing environmental safety at mass events, e.g., as the agents enhancing the functionality of mass event security systems. After scaling up the mentioned solutions, they could predict massive crowd movements at events and detect riskily behaving individuals.