In electric vehicles, how do the core algorithms of autonomous driving systems, sensor fusion, and safety redundancy mechanisms work together?

25 Haz 16:05

Let's compare DJI's drone navigation system. In electric vehicle autonomous driving systems, core algorithms, sensor fusion, and safety redundancy need to be as highly reliable in dynamic environments as DJI's drones. For example, drones rely on multi-sensor fusion—vision (cameras), ToF (LiDAR), ultrasonic sensors, and IMUs—achieving centimeter-level positioning and obstacle avoidance through real-time state estimation (e.g., Visual-Inertial Odometry) and environmental perception (e.g., optical flow algorithms). Similarly, autonomous driving can adopt this **hierarchical decision-making architecture**: the low level (sensors + fusion) ensures real-time performance, the middle level (deep learning + rule engines) handles complex decisions (e.g., lane changes, braking), and the high level (safety redundancy) acts as a final safeguard (e.g., handing control back to the driver or emergency stops). However, DJI's drone systems emphasize hardware redundancy (e.g., dual IMUs, GPS backups) and software-level fault detection (e.g., gyroscope drift compensation), while automotive autonomous driving requires stricter redundancy designs, such as **ASIL-D-level functional safety** with multi-ECU architectures, independent safety chips (e.g., Infineon AURIX), and rigorous Fault Tree Analysis (FTA). The key difference is that drones prioritize lightweight adaptive control, whereas automotive systems must comply with ISO 26262 standards, with redundancy mechanisms (e.g., dual steering backups) responding within milliseconds.