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New driving force for autonomous driving: How does the point cloud algorithm release the power of laser radar?
In 2015, the automotive industry was still debating whether autonomous driving should rely on laser radar or cameras. By 2016, things had changed significantly, especially after a high-profile incident involving an autopilot system. This event led to a growing recognition of the importance of laser radar in the development of self-driving technology. Let’s explore how this sensor plays a key role in the evolution of autonomous vehicles.
Lidar has become an essential component in the perception layer of autonomous systems. As shown in the diagram, the system integrates multiple sensors such as lidar, cameras, millimeter-wave radars, GPS, encoders, and IMUs. These inputs are processed through advanced algorithms that distinguish between static and dynamic objects, enabling accurate object detection, classification, and tracking.
The image below illustrates the architecture of the perception layer in autonomous driving systems. High-precision maps are created using data from lidar and cameras, which are then combined with real-time sensor data for localization and path planning. The vehicle's control is ultimately managed via its CAN bus, ensuring smooth operation.
In terms of perception, lidar, cameras, and millimeter-wave radars each play unique roles. Lidar excels at identifying, classifying, and tracking objects, while cameras are effective for object classification and tracking. Millimeter-wave radars are primarily used for obstacle detection, particularly in challenging weather conditions.
Although these three sensors have overlapping functions, they also complement each other. For instance, lidar provides precise 3D spatial information, cameras offer rich visual details, and millimeter-wave radars are reliable for detecting obstacles at longer distances.
There are two main categories of lidar: scanning and non-scanning. Scanning lidars, such as mechanical rotating units, are currently more mature and widely used in autonomous applications. MEMS-based and phased array lidars are emerging technologies that aim to reduce costs and improve performance. Non-scanning lidars, like Flash LiDAR, emit light across a wide area, offering faster data acquisition.
Currently, the industry is pushing for cost reduction and mass production of lidar systems. Solid-state lidar is gaining traction, promising faster development and commercialization. With support from the entire supply chain, we can expect affordable lidar solutions in the near future.
Point cloud data, generated by lidar or depth cameras, captures the spatial distribution of objects. Each point includes 3D coordinates and intensity values, making it ideal for mapping and navigation.
One of the most critical applications of lidar is positioning. By combining data from IMU, GPS, and lidar, vehicles can determine their exact location. This process involves matching real-time point cloud data with high-precision maps to achieve accurate localization.
Another important use is obstacle detection and classification. Unlike vision systems, lidar operates independently of lighting conditions and offers a 360-degree field of view. It can detect and classify objects in real time, making it a reliable tool for autonomous navigation.
Despite its potential, lidar faces challenges such as high costs, limited availability, and immature algorithms. To address these issues, companies are working on open platforms like the Prometheus initiative, aiming to accelerate the adoption of lidar in autonomous systems.
Lidar is also used for lane detection and roadside identification. By analyzing reflection intensity and geometric features, lidar can accurately extract lane lines and road edges, even under varying lighting conditions.
Object tracking using lidar involves identifying individual objects and monitoring their movement over time. This enables the vehicle to track surrounding traffic, calculate speeds, and maintain safe distances.
In conclusion, lidar is a powerful sensor that enhances the capabilities of autonomous vehicles. From positioning and obstacle detection to lane recognition and object tracking, it plays a vital role in the development of self-driving technology. As the industry continues to evolve, lidar will remain a key driver in shaping the future of mobility.