Deep Neural Networks for Autonomous Vehicles: A Review of Advances, Challenges and Future Directions

Deep Neural Networks (DNNs) have become a vital technology in the quick development of autonomous cars, enabling vision, decision-making, and control systems. This has created new avenues for exploring intelligent traffic safety, comfortable travel, and smart roadways. Because of its enormous potential to decrease traffic accidents and injury to people, autonomous vehicles have recently become a very attractive study topic. However, object detection, scene identification, lane recognition, and additional challenges are crucial for self-driving cars to solve. The development, difficulties, and probable future directions of DNNs for driverless vehicles are thoroughly examined. Perception tasks, sensor fusion, safety issues, decision-making algorithms, and the incorporation of DNNs into practical autonomous driving systems are just a few of the many areas it addresses. This review attempts to offer insights and suggestions for more breakthroughs in the field through the investigation of the most recent state-of-the-art research and highlighting significant difficulties.