Detecting What Matters: A Novel Approach for Out-of-Distribution 3D Object Detection in Autonomous Vehicles

--Autonomous vehicles (A Vs) use object detection models to recognize their surroundings and make driving decisions accordingly. Conventional object detection approaches classify objects into known classes, which limits the A V's ability to detect and appropriately respond to Out-of-Distribution (OOD) objects. This problem is a significant safety concern since the A V may fail to detect objects or misclassify them, which can potentially lead to hazardous situations such as accidents. Consequently, we propose a novel object detection approach that shifts the emphasis from conventional class-based classification to object harmfulness determination. Instead of object detection by their specific class, our method identifies them as either harmful or harmless based on whether they pose a danger to the A V . This is done based on the object position relative to the A V and its trajectory. With this metric, our model can effectively detect previously unseen objects to enable the A V to make safer real-time decisions. Our results demonstrate that the proposed model effectively detects OOD objects, evaluates their harmfulness, and classifies them accordingly, thus enhancing the A V decision-making effectiveness in dynamic environments. UTONOMOUS vehicles (A Vs), also known as self-driving cars, have the potential to revolutionize transportation by partially or completely replacing the human drivers [1]. They operate using a variety of sensors, advanced artificial intelligence (AI), including machine learning (ML), algorithms, and other classical solutions to navigate their environment, make decisions, and control operations.