Core Viewpoint - The article discusses the transition of LiDAR technology from high-cost to more affordable options, highlighting its growing adoption in the automotive industry and the challenges of integrating LiDAR with camera-based systems for autonomous driving [3][8][90]. Group 1: Signal Conflict - The article emphasizes the inherent signal conflicts between LiDAR and cameras, as they produce fundamentally different data types: cameras generate 2D pixel matrices while LiDAR produces 3D point cloud data [19][20]. - It explains that while LiDAR has superior 3D resolution, it lacks color information, making it unable to recognize traffic lights and signs, which necessitates the use of cameras as the primary sensor in most autonomous driving systems [21][22]. - The differences in data generation frequency between cameras (typically over 60Hz) and LiDAR (often below 30Hz) can lead to synchronization issues, potentially causing significant errors in high-speed driving scenarios [27][28]. Group 2: Alignment and Integration - The article highlights the importance of aligning the data granularity between LiDAR and cameras to maximize the effectiveness of the sensor fusion in autonomous driving systems [35][36]. - It discusses the evolution from traditional logic-based algorithms to end-to-end neural network systems, which can directly process 2D images without converting them to 3D, posing challenges for integrating LiDAR data [42][44]. - Techniques such as voxelization are mentioned as methods to convert 3D LiDAR data into a 2D format that can be more easily integrated into the system [48]. Group 3: Confidence Systems - The article describes the implementation of a confidence system that dynamically adjusts the trust level assigned to LiDAR and camera data based on environmental conditions, enhancing decision-making in various scenarios [62][65]. - It explains that in adverse conditions, such as low light, the system may prioritize LiDAR data, while in situations where LiDAR performance is compromised, the camera's confidence may be increased [66][69]. Group 4: LiDAR Line Count - The article addresses the misconception that a higher line count in LiDAR necessarily equates to better performance, noting that increased line counts lead to higher data processing demands, which can strain system resources [75][81]. - It emphasizes the need for a balance between LiDAR resolution and system processing capabilities to ensure timely and accurate responses in autonomous driving applications [86][87]. Group 5: Future Outlook - The article concludes that while camera-based systems currently hold an advantage in terms of cost and simplicity for Level 2 driving assistance, LiDAR technology has significant long-term potential to surpass human driving capabilities as integration challenges are overcome [88][90].