Core Viewpoint - The article discusses the impact of end-to-end autonomous driving technology on traditional rule-based control (PNC) methods, highlighting the shift towards data-driven approaches and the complementary relationship between the two systems [2][6]. Summary by Sections Differences Between Approaches - Traditional PNC relies on manually coded rules and logic for vehicle planning and control, utilizing algorithms like PID, LQR, and various path planning methods. Its advantages include clear algorithms and strong interpretability, suitable for stable applications [4]. - End-to-end algorithms aim to directly map raw sensor data to control commands, reducing system complexity and enabling the model to learn human driving behavior through large-scale data training. This approach allows for joint optimization of the entire driving process [4]. Advantages and Disadvantages - **End-to-End Approach**: - Advantages include reduced system complexity, natural driving style emulation, and minimized information loss between modules [4]. - Disadvantages involve challenges in traceability of decision processes, high data scale requirements, and the need for rule-based fallback in extreme scenarios [4]. - **PNC Approach**: - Advantages include clear module functions, ease of debugging, and stable performance in known scenarios, making it suitable for high safety requirements [5]. - Disadvantages consist of high development costs and potential difficulties in handling complex scenarios without suitable rules [5]. Complementary Relationship - The analysis indicates that end-to-end systems require PNC for certain scenarios, while PNC can benefit from the efficiencies of end-to-end approaches. This suggests a complementary rather than adversarial relationship between the two methodologies [6]. Job Opportunities - The article highlights job openings in both end-to-end and traditional PNC roles, indicating a demand for skilled professionals in these areas with competitive salaries ranging from 30k to 100k per month depending on the position and location [8][10][12][14].