The development of autonomous electric vehicles has gained significant attention due to their potential to reduce carbon emissions and improve road safety. Image processing has become an important tool in the development of these vehicles, enabling them to detect and respond to objects and obstacles in their environment. In this review paper, we explore the use of image processing in electric vehicles and driverless cars, with a focus on the various techniques proposed by authors. The comparison of the performance and effectiveness of different approaches, including deep learning, computer vision, and sensor fusion, in detecting and recognizing objects in the environment. Our review highlights the advantages and limitations of each technique and their potential for future development in the field of electric vehicles. Overall, image processing has shown to be a promising solution for the development of safe and efficient autonomous electric vehicles.
Top1. Introduction
Image processing is critical for autonomous driving in both electric vehicles and driverless automobiles. Image processing algorithms can extract meaningful information about the vehicle's environment and make educated judgements about its motions and interactions with other objects by utilizing various sensors such as cameras, LiDAR, and radar. [1] Object detection algorithms can identify and track various objects such as other vehicles, pedestrians, and obstacles, allowing the vehicle to make appropriate decisions about its speed and direction of travel. Lane detection algorithms can detect and track road markings, helping the vehicle to stay within its lane and maintain safe distances from other vehicles. [2] Figure 1 shows vehicle controlled by image processing
Figure 1. Vehicle controlled by image processing
Traffic sign recognition algorithms can identify and interpret road signs, enabling the vehicle to understand speed limits, stop signs, and other traffic signals. These algorithms can also help the vehicle to adjust its speed and trajectory accordingly. [3]
Real-time processing algorithms are particularly important for autonomous driving, as they must operate quickly and efficiently in order to make decisions in real-time. This requires sophisticated algorithms and powerful computing hardware, but the results can be significant, enabling safer and more efficient driving in a range of conditions. Figure 2 shows real-time processing using input image. [4-5]
Figure 2. Real time control using image processing algorithms
Overall, image processing is a critical component of autonomous driving systems in both electric vehicles and driverless cars, and it will continue to play an important role as these technologies evolve and become more widespread. By improving the accuracy and reliability of object detection, lane detection, and other key functions, image processing algorithms will help to bring us closer to a future of safer, more efficient, and more convenient transportation.
The use of multiple sensors such as cameras, LiDAR, and radar can provide a comprehensive and accurate view of the vehicle's surroundings, enabling it to make informed decisions about its movements and interactions with the environment. [6] Each sensor type has its own strengths and weaknesses, and combining them can provide a more robust and reliable system for electric vehicles.
Cameras are useful for providing high-resolution images of the environment, allowing for detailed object detection, recognition, and tracking. They are also relatively inexpensive and widely available, making them a popular choice for many autonomous vehicle applications. However, cameras can be limited by poor visibility in adverse weather conditions, and they can struggle to detect certain types of objects such as low-contrast or reflective surfaces.
LiDAR, on the other hand, uses laser beams to create a 3D map of the environment, providing accurate distance and depth measurements. [7-8] This makes it ideal for detecting and tracking objects in complex and dynamic environments, such as urban areas or construction sites. However, LiDAR sensors can be expensive and are often larger and more cumbersome than cameras or radar sensors.
Radar sensors use radio waves to detect objects in the environment, making them useful for detecting objects at longer distances or in adverse weather conditions. They are also relatively low-cost and can be easily integrated into existing vehicle systems. However, radar sensors can struggle to detect certain types of objects such as small or low-reflectivity objects.
By combining these sensors, electric vehicles can benefit from the strengths of each technology while minimizing their weaknesses. For example, cameras can be used to provide detailed object detection and recognition, while LiDAR and radar sensors can be used for distance measurements and object tracking. [9] This can provide a more robust and reliable system for autonomous driving, improving safety and efficiency on the road.