Pedestrian detection refers to the technology and methods used to identify and track pedestrians in a given environment, typically in the context of advanced driver assistance systems (ADAS) or autonomous vehicles. The primary goal of pedestrian detection is to enhance road safety by providing real-time information about the presence and location of pedestrians, allowing the vehicle to take appropriate actions to avoid collisions.
Vulnerable Road Users (VRUs) are a crucial subset of pedestrians that require special attention. VRUs include individuals such as cyclists, motorcyclists, and individuals with mobility aids. They are more vulnerable to accidents due to their smaller size, unpredictability, and potential for greater injury. Therefore, pedestrian detection systems must be designed to accurately detect and track VRUs to ensure their safety on the road.
One way in which artificial intelligence (AI) can improve pedestrian detection is through the use of deep learning algorithms. Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated excellent performance in object detection tasks, including pedestrian detection. By training these algorithms on large datasets containing diverse pedestrian and VRU examples, AI can learn to accurately detect and classify pedestrians and VRUs in various scenarios, including different lighting conditions, weather conditions, and complex urban environments.
In addition to detection, AI can also enhance pedestrian safety through Automatic Emergency Braking (AEB) systems. AEB systems use sensors, such as cameras and radar, to monitor the road and detect potential collisions. When a pedestrian or VRU is detected in a critical situation, the AI-powered AEB system can initiate emergency braking to minimize or prevent a collision. AI algorithms can analyze the real-time data from sensors and make quick decisions to activate the braking system with high precision and speed, potentially saving lives and reducing the severity of accidents.