Object Detection in Autonomous Vehicles: Status and Open Challenges
It addresses the problem of safe and robust perception for autonomous driving, but is incremental as it focuses on summarizing existing work and challenges.
This article reviews the state-of-the-art in object detection algorithms, particularly deep learning-based methods, for autonomous vehicles to perceive surroundings like pedestrians and vehicles, but does not present new experimental results or concrete numbers.
Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.