# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 1 Introduction  
With the proliferation of electric vehicles, the maturation of autonomous driving, and the development of valet parking technologies, extensive research has been conducted on automatic charging solutions for electric vehicles. In the rapidly advancing field of artificial intelligence, the utilization of robots for the automatic charging of electric vehicles is regarded as an ideal solution. Currently, research in the domain of automatic electric vehicle charging primarily focuses on the design of charging robots [1; 2] and the visual positioning of charging ports [3; 4; 5; 6; 7]. However, a notable gap remains in the research concerning the safety aspects of robots during the charging plug-in process. When addressing the safety of the automatic charging plug-in process, collisions involving robots become an inevitable challenge. Despite the generally reliable precision of visual systems in providing accurate charging port localization, external environmental factors, such as extreme lighting conditions, temperature variations, and humidity, can lead to visual positioning failures. In such situations, the absence of an effective collision protection system poses a risk of damage to the robot and the vehicle. Currently, research on the issue of robot collisions is primarily concentrated in the field of physical human-robot interaction (pHRI) [8]. In this field, robots are often employed in unstructured and highly dynamic environments, where contact or collisions with humans are sometimes unavoidable. Due to the diverse forms and processes of collision contact, there is currently no one-size-fits-all approach to handling such situations. In [9], an insightful collision processing pipeline is proposed, breaking down the core processes of collision into collision detection [10; 11], collision localization [12; 13], collision identification [8; 14], collision classification [15; 16], and reaction after collision [17; 18]. Among these, collision classification holds significant importance in ensuring human-robot safety. Its purpose is to predict the severity of the potential damage caused by a collision or the acceptability of the collision contact. This step serves as a direct decision-making point for the robot to respond appropriately in the event of a collision. Effective collision classification methods can substantially enhance the safety of robot systems. Although the objectives of the collision processing stages mentioned above differ, there is a certain degree of commonality in the methods employed across these processing stages. These methods are primarily categorized into model-based collision processing methods, tactile-sensor-based collision processing methods, and data-driven collision processing methods [15]. Model-based collision processing methods offer the advantage of efficiently utilizing onboard sensors to address collision issues without the need for external sensor intervention, thereby significantly reducing the cost of collision issue resolution [10; 17]. However, their effectiveness in addressing collision problems is constrained by differences between the constructed observation model and the actual physical characteristics, resulting in limited precision in problem resolution. In contrast, the use of tactile sensors often provides a precision advantage in handling collision problems [19; 20]. These sensors are typically deployed in the form of artificial skin on robots, enabling more refined collision processing. However, the manufacturing process for such artificial skin is often complex, and these sensors may not be suitable for scenarios involving high loads and frequent contact due to their direct interaction with the target being manipulated or protected. With the advancement of artificial intelligence technology, supervised learning-based methods are increasingly being introduced when addressing collision problems [8; 21]. These methods, collectively referred to as data-driven collision processing methods, can be implemented using both onboard and external sensors.