Preliminary Exploration of Object Detection Techniques for Autonomous Driving

Object detection is essential for autonomous vehicle (AV) safety, underpinning tasks like path planning, collision avoidance, and motion prediction. This ongoing research presents preliminary findings from a concise survey of object detection methodologies, including image-based, 3D detection, and hybrid approaches. By reviewing performance metrics such as accuracy, speed, and robustness, it explores trade-offs among methods and identifies initial opportunities for optimization. Challenges such as computational demands, robustness in adverse conditions, and data diversity limitations are discussed alongside potential strategies like lightweight model optimization and multimodal sensor fusion. These preliminary insights aim to guide future research in developing scalable and efficient detection systems for safer AV deployment.