[PERSON]  
1 Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea; [EMAIL_ADDRESS] (S.-H.P)  
[PERSON]  
1 Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea; [EMAIL_ADDRESS] (S.-H.P)  
[PERSON]  
1 Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-siCameras, by contrast, excel at visual object detection and tracking, making them indispensable for recognizing traffic signs, pedestrians, and vehicles, as well as analyzing movement patterns. They offer a visual understanding that complements other sensors, capturing rich data that can be processed through machine vision algorithms. Ultrasonic sensors use high-frequency sound waves for precise short-range detection. They are highly effective for proximity sensing and obstacle avoidance at close distances, making them suitable for tasks that require high accuracy in confined spaces. Infrared sensors detect radiation emitted by objects, allowing for distance estimation based on temperature variations. This capability proves valuable in applications like industrial temperature monitoring and identifying heat signatures, where thermal information can guide safe operations or improve detection accuracy. On the other hand, radar sensors (Radio Detection and Ranging) employ radio waves to detect objects, measuring distances and velocities by capturing the time taken for an emitted pulse to return as an echo. Radars are particularly effective under challenging weather conditions, such as rain or fog, and provide reliable performance in scenarios where visibility is compromised [5]. Recent studies have demonstrated the effectiveness of radar in autonomous vehicle systems, particularly for detecting and classifying pedestrians and other vehicles in driving assistance applications. For instance, in [6], a neural network model identifies objects, such as pedestrians and vehicles, using radar data. The radar sensors measure the distance to objects and analyze the reflected signal power, enabling accurate object classification. A set of 2131 different objects were detected during driving, achieving a high detection accuracy of approximately 91% across diverse driving scenarios, including tests at different times during the day and night. In [7], researchers introduced a method for generating grid maps from sparse 3D radar point clouds collected from vehicles. The experimental setup involved a vehicle equipped with two radar sensors: a 77 GHz FMCW radar with MIMO technology (48 Tx, 48 Rx) and a 360\({}^{\circ}\) monostatic rotating radar. These sensors, along with a lidar unit for ground-truth comparison, were used to map an outdoor environment featuring natural elements such as trees, bushes, and grass, while the vehicle traversed a 600 m uneven, unpaved road at a speed of 2.5 m/s. The study highlights the feasibility of radar-based topographic mapping for autonomous navigation and robotics. Building on advancements in sensor technology, these individual capabilities can be significantly enhanced through integration and fusion. Combining data from multiple sensor types allows robots to achieve a more comprehensive understanding of their surroundings, improving decision-making capabilities and addressing the limitations inherent to single sensors [8]. Sensor fusion not only improves the accuracy of detection but also addresses the limitations inherent to individual sensor types, thereby enhancing overall system resilience in varied and dynamic environments. Complementing these hardware advancements, deep reinforcement learning (DRL) has emerged as a powerful tool for enabling robots to make intelligent decisions in complex environments. DRL allows robots to learn from interactions within their environment, adapt to new conditions, and develop strategies to optimize navigation paths autonomously [9].