Sensor Fusion

Sensor Fusion refers to a core technology that processes raw data or feature inputs gathered from various independent sensors of different types and mounting locations (such as optical cameras, millimetre-wave radars, LiDARs, ultrasonic sensors, and IMU inertial measurement units). Through computer algorithms, it executes spatiotemporal synchronisation, complementary calibration, and consolidated processing to deliver high-precision, highly redundant, all-weather environmental perception. In Advanced Driver Assistance Systems (ADAS) and higher-level autonomous driving (Level 3 and above), individual sensors have inherent physical limitations—cameras struggle with glare and low light, radar offers limited resolution, and LiDAR is costly and vulnerable to adverse weather. Sensor fusion bridges these gaps by combining their strengths to achieve a more accurate and robust digital reconstruction of the physical surroundings.

Core Technical Architecture and Operating Principles

The technical architecture of sensor fusion is typically divided into three primary tiers: data-level fusion, feature-level fusion, and decision-level fusion:

Multi-Source Heterogeneous Data Spatiotemporal Alignment and Complementarity:

Autonomous driving systems require high-precision alignment of sensor data across varying sampling frequencies and coordinate frameworks. For instance, ultrasonic sensors handle close-range blind-spot detection; millimetre-wave radar provides strong penetration and excels in velocity tracking and long-range distance measurement, yet struggles with object classification; while optical cameras deliver ultra-high resolution, colour, and texture recognition. Using Kalman Filters, deep learning networks, or Bayesian estimation, fusion algorithms seamlessly merge the radar's precision in range and speed with the camera's semantic classification, accurately mapping obstacle locations even during visual obstruction or partial sensor failure. 

Multi-Dimensional Redundancy Verification and Advanced State Estimation:

In complex dynamic driving environments, algorithms establish a unified world coordinate system to track fused targets. Through multi-sensor cross-validation, the system effectively eliminates false positives and false negatives while calculating precise real-time trajectories, acceleration rates, and predictive intentions of surrounding road users. This provides a highly reliable operational baseline for autonomous driving domain controllers (such as smart driving domain controllers) to execute safe driving decisions. 

Safety Regulations and Physical Limitations 

Never Blindly Rely on Sensor Fusion-Dependent ADAS Functions Under Severe Windscreen Damage, Obscured Camera Lenses (e.g. Mud or Snow), Front Bumper Radar Misalignment from Impact, or Sensor Calibration Loss: 

Sensor fusion relies heavily on the integrity and health of its data sources. Never operate smart driving systems with existing hardware faults. When a core sensor delivers compromised or distorted data due to physical blockage or damage, any failure by the fusion algorithm to identify and filter out these erroneous inputs (i.e. "garbage in, garbage out") can trigger catastrophic perception misjudgements, leading to severe rear-end collisions or impacts.

Never Relinquish Full Vehicle Control to Autonomous Driving Systems Without Remaining Ready for Immediate Manual Takeover During Extreme Weather (e.g. Torrential Downpours or Blizzards Causing LiDAR Attenuation and Camera Blindness) or Highly Reflective Scenarios Causing Multi-Sensor Blind Spots: 

No matter how advanced, sensor fusion cannot overcome the fundamental laws of physics. Drivers must never lower their guard when operating beyond physical threshold limits. When extreme conditions blind multiple sensors simultaneously, the system will trigger a degraded failsafe mode or issue handover warnings. The driver must keep both hands on the steering wheel at all times, maintaining full visual oversight of road conditions and retaining ultimate responsibility for vehicle control.