Monitoring

These applications demand processing of multiple video feeds and even a 360-degree view of the vehicle. ​The Hailo AI processors was designed for scalability and can support the demanding deep learning workloads that ADAS (Advanced Driver Assistance Systems) require. One small, low-power chip is able to process multiple video streams and multiple chips can work in tandem or cascade, all while maintaining industry-leading efficiency and scalability, high processing throughput and low latency. The solution’s flexibility allows the use of different types of camera inputs, ranging in resolution and input size.​ These outstanding capabilities make the Hailo-8 a market-leading AI processor for ADAS ECUs. ​

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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.

Lane keeping refers to a technology designed to assist drivers in maintaining their vehicle within the designated lane on the road. It relies on sensors and cameras to monitor lane markings and provides alerts or corrective actions if the vehicle drifts out of the lane unintentionally. AI can significantly enhance lane keeping systems by improving their accuracy and responsiveness. AI algorithms can analyze real-time data from multiple sensors to precisely detect lane boundaries, even in challenging conditions like poor visibility or faded road markings.

Furthermore, AI can continuously learn and adapt from driving patterns, allowing the system to provide more personalized and efficient assistance to drivers. By leveraging AI, lane keeping technologies can enhance road safety, reduce accidents caused by lane departures, and contribute to a more secure and comfortable driving experience.

Path planning determines the optimal trajectory for a vehicle to follow in order to reach its destination while considering various factors such as road conditions, traffic, and obstacles. AI enhances path planning by utilizing machine learning algorithms to analyze vast amounts of data, including historical traffic patterns, road information, and sensor inputs, enabling vehicles to make intelligent decisions in real-time. This allows for more efficient and adaptive path planning, minimizing travel time, reducing congestion, and improving overall safety on the roads.