# Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
## 1 Introduction
This research seeks to determine the correct O-D matrix from traffic counts and the optimal number and placement of sensors for efficient data gathering. Despite this innovative approach, the study may face challenges, such as the need for high-quality historical traffic data for model training, computational complexity of the deep learning model, and practical constraints of sensor placement in real-world scenarios. [PERSON] et al. (2023) developed SafePathNet, a novel machine-learning system designed for trajectory prediction and planning in AVs [13]. SafePathNet utilizes a unified neural network to anticipate future pathways for AVs and the surrounding road agents to avoid conventional rule-based methodologies. It integrates the attention mechanism of a transformer module with an expert mixing strategy to predict different trajectories and prioritize safety and reliability. SafePathNet chooses a trajectory in real-time decision-making based on safety metrics and the predicted likelihood. Simulators and real-world tests have underscored their safety and effectiveness, distinguishing them from contemporary data-driven methods. However, this study did not focus on achieving an ADS level of 4+. By leveraging deep reinforcement learning, the proposed system significantly advances the adaptive capabilities of AVs, aiming at ADS Level 4+ autonomy. Deep reinforcement learning overcomes the limitations of heuristic and static rule-based systems, such as fuzzy logic and expert systems, enabling AVs to interpret extensive sensor data and iteratively refine their driving strategies from real-world interactions. Although fuzzy logic copes with ambiguity, and expert systems execute preprogrammed rules, they cannot self-optimize in response to novel stimuli. However, our deep reinforcement learning approach excels in assimilating experiences from unpredictable and intricate driving conditions, fostering a learning mechanism that continually enhances decision-making and adaptability. This empowers AVs with the decision-making agility for high-risk environments, ensuring robust performance across a diverse spectrum of real-world driving situations to achieve ADS Level 4+. Second, a validation phase for AVs and their ADS is imperative to ensure their safety, reliability, and functionality. Although a wide array of tests is utilized in the development and validation process, the two critical testing methods for AVs and ADS are (1) miles driven [14; 15; 16; 17; 18] and (2) scenario-based testing [19; 20; 21; 22; 23; 24]. The other validation approaches are presented in Appendix A. _Miles-driven validation_ quantifies the performance of an ADS by measuring the distance traveled by an AV without significant safety incidents. This method offers a direct and easily comprehensible metric and provides an ADS with a genuine and varied real-worlddriving experience. However, these methods exhibit several limitations. Relying solely on mileage does not necessarily represent the diversity and complexity of the driving scenarios a vehicle encounters. Moreover, accruing substantial mileage for testing can be both resource intensive and time consuming, particularly when the objective is to validate a system's response to rare events. [PERSON] et al. (2017) asserted that evaluating the safety of AVs utilizing a mile-driven approach is problematic ([PERSON] et al., 2017). This method measures the number of miles that an AV drives without an accident and compares its safety with that of human drivers. To statistically prove that an AV is as safe as a human, it would need to drive 30 million miles, given a specific fatality probability. If the goal is for AVs to become significantly safer, this will increase to 30 billion miles. In addition, any software update can render previous tests obsolete, and there is concern regarding whether these miles genuinely represent real-world conditions. _Scenario-based validation_ emphasizes the evaluation of an ADS within specific predefined scenarios.