New AI System Enhances Road Safety with Real-Time Pedestrian and Sign Detection

Researchers from the University of Debrecen, Hungary, have developed an affordable, universally applicable AI system for real-time pedestrian and priority sign detection. The system, designed to enhance safety in autonomous driving, uses two cameras, an NVIDIA Jetson Nano B01 low-power edge device, and an LCD display. It focuses on identifying pedestrians, pedestrian crossings, signs, and road paintings separately, stop signs, and give way signs using a custom-trained CNN known as SSD-MobileNet. The system’s affordability and universal applicability could make it a cost-effective alternative to existing advanced driver assistance systems (ADASs).

What is the New AI System for Real-Time Pedestrian and Priority Sign Detection?

The article discusses a new embedded system designed for real-time pedestrian and priority sign detection. This system, developed by Kornel Sarvajcz, Laszlo Ari, and Jozsef Menyhart from the University of Debrecen, Hungary, aims to enhance safety in autonomous driving and reduce dependence on human intervention. The system is affordable and universally applicable across various vehicles, making it accessible to a substantial portion of the population.

The system comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device, and an LCD (liquid crystal system) display. It is designed to seamlessly integrate into a vehicle without occupying substantial space, providing a cost-effective alternative to existing advanced driver assistance systems (ADASs) that often incorporate costly components. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians.

The system stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection. It concentrates on identifying five crucial objects: pedestrians, pedestrian crossings, signs, and road paintings separately, stop signs, and give way signs. Object detection is executed using a lightweight custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet implemented on the Jetson Nano.

How Does the New AI System Work?

To tailor the model for this specific application, the pretrained neural network underwent training on a custom dataset. This dataset consists of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation. Its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.

The system uses the latest computer vision technologies to process and analyze images. External environment sensing involves the collection of information about the driving environment, encompassing nearby vehicles, pedestrians, traffic signs, traffic lights, and various objects. The design of such systems must consider various factors, including weather, road, and lighting conditions, as these elements can significantly impact the perceptual quality of environmental information.

Over the past decade, there has been a noteworthy emphasis on the development of traffic surveillance systems directed towards enhancing safety through the real-time monitoring of the road environment. The availability of multiple sensing modalities such as radar, LIDAR (light detection and ranging), and cameras, coupled with the increase in computing power, has empowered engineers to devise increasingly sophisticated solutions for real-time computer vision-based assistance systems.

What is the Impact of CNN Deployment in Automotive?

As technology advances, the expectations of car buyers evolve with it. The growth of safety and comfort-enhancing technologies in the automotive sector has led to a pronounced focus on the study and development of these systems. They have evolved into a pivotal area of research due to their capacity to enhance the overall driving experience.

While various technologies contribute to the creation of these systems, camera-based solutions offer significant cost advantages. Significant strides in vehicular sensor technology have been documented, enabling the execution of various simple and complex tasks, including object detection, localization, tracking, and activity recognition across a diverse array of applications.

The deployment of Convolutional Neural Networks (CNN) in automotive applications has been a game-changer. CNNs are a class of deep learning neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks, based on their shared-weights architecture and translation invariance characteristics. In the context of the new AI system, CNNs play a crucial role in object detection and recognition, contributing significantly to enhancing road safety.

How Does the New AI System Enhance Road Safety?

The new AI system is designed to enhance road safety by providing real-time pedestrian and priority sign detection. By identifying crucial objects such as pedestrians, pedestrian crossings, signs, and road paintings separately, stop signs, and give way signs, the system can alert the driver or the autonomous driving system in real-time, thereby preventing potential accidents.

The system’s ability to function under diverse lighting and traffic conditions further enhances its effectiveness. By training the system with a custom dataset of images captured under various conditions, the researchers have ensured that the system can accurately detect and recognize objects in different scenarios.

The system’s affordability and universal applicability across various vehicles also contribute to its potential in enhancing road safety. By providing a cost-effective alternative to existing ADASs, the system can be more widely adopted, thereby benefiting a larger portion of the population.

What is the Future of AI in Autonomous Driving?

The research by Sarvajcz, Ari, and Menyhart represents a significant advancement in the field of autonomous driving. By developing an affordable and universally applicable system for real-time pedestrian and priority sign detection, they have made a noteworthy contribution to enhancing the safety and accessibility of autonomous driving technologies.

The use of AI in autonomous driving is expected to continue growing in the future. With advancements in technologies such as CNNs and increased computing power, engineers can devise increasingly sophisticated solutions for real-time computer vision-based assistance systems.

The future of AI in autonomous driving also lies in the development of systems that can adapt to various factors, including weather, road, and lighting conditions. By considering these elements in the design of AI systems, researchers can ensure that these systems can accurately perceive and respond to their environment, thereby enhancing their effectiveness in enhancing road safety.