Core Viewpoint - Momenta's autonomous driving solution utilizes a mapless approach, focusing on real-time data collection and processing to enhance vehicle navigation and safety without relying on pre-built high-precision maps [4][5][14]. Implementation Plan - The solution involves several key steps: data collection through various sensors, perception processing using computer vision and deep learning, precise localization through sensor fusion, and trajectory planning that considers traffic rules and passenger comfort [4][5][6]. Data Collection and Sensor Input - Vehicles are equipped with multiple cameras, LiDAR, radar, IMU, wheel speed sensors, and GNSS receivers to continuously gather environmental and vehicle status data [5]. - The perception module processes this data to detect and classify objects, generating a local map that reflects the current environment dynamically [5]. Localization and Path Planning - The localization module combines IMU, wheel speed, and GNSS data to calculate the vehicle's precise position and orientation using filtering and optimization algorithms [5]. - Path planning generates detailed driving trajectories based on global routes and local maps, outputting control commands for steering, acceleration, and braking [5]. System Integration and Validation - The DDLD architecture is a data-driven landmark detection system that identifies and classifies road elements for autonomous driving and high-precision map construction [8][10]. - The system undergoes extensive real-world testing to ensure reliability and safety across various driving scenarios [10]. Automation and Data-Driven Loop - The entire system operates as a data-driven closed-loop, addressing the challenge of efficiently acquiring large volumes of high-precision labeled data for autonomous driving [14][15]. DDLD Model Architecture - The DDLD model employs a feature extraction phase followed by a decoding phase that utilizes learnable queries instead of traditional anchor boxes, enhancing detection performance [22][23][29]. Planning and Decision-Making - The planning process is described as a high-dimensional joint search problem, requiring consideration of time, space, and multiple action dimensions [35]. - Deep learning planning is proposed as a more efficient solution, potentially improving planning performance and efficiency [33]. Data Quality and Pipeline Solutions - A robust automated data production pipeline is essential for ensuring data quality, addressing issues such as data imbalance, simulation drift, and conflicting data [44][45]. - Various data pipelines are designed to tackle specific data challenges, ensuring the model's adaptability and accuracy [45]. Performance Evaluation - The DLP (Deep Learning Planning) model demonstrates significant improvements in safety and comfort metrics compared to traditional rule-based planning methods, particularly in complex scenarios like cut-in events [56][61]. - The model's success rates and reliability metrics indicate a substantial enhancement in handling challenging driving situations, showcasing its robustness and effectiveness [65].