3. Sensing architecture for occupant classification

An adaptive restraint system can only modulate its deployment if it first knows who is sitting in the seat. Occupant classification (OC) is therefore the perceptual front end of the entire restraint chain: airbag suppression, dual-stage inflator selection, and seat belt pretensioner force limiting all depend on a reliable estimate of occupant size, position, and posture. This chapter examines the four dominant sensing modalities used in production OC architectures — pressure mats, capacitive sensors, vision systems, and seat belt load cells — and explains how they are combined into a fused classification decision.

Why a Single Sensor Is Never Enough

Each modality observes a different physical quantity and therefore has a distinct failure mode. A pressure mat measures distributed normal force but cannot distinguish a rear-facing child seat from a heavy bag. A capacitive sensor detects dielectric coupling and can classify occupancy type, yet it is sensitive to humidity and spilled liquids. A vision system resolves posture and position with high spatial resolution but degrades in low light and raises privacy concerns. A seat belt load cell measures the tension actually transferred through the webbing, which is the most direct proxy for occupant mass in a belted condition, but it is blind to unbelted occupants.

Because no single channel is robust across all conditions, production systems fuse at least two, and often three, modalities. The fusion layer does not simply average signals; it applies a decision logic that weights each channel according to its confidence in the current operating state.

Pressure Mats (Force-Sensing Resistor Arrays)

A pressure mat is a matrix of force-sensing resistors (FSRs) laminated into the seat cushion. Each cell changes resistance inversely with applied normal force. A typical production mat uses a 4×4 or 8×8 grid, giving 16 to 64 sensing cells. The mat is scanned by a multiplexer, and each cell is converted to a force estimate through a calibration curve.

The raw output is a pressure distribution map. From this map the OC algorithm extracts features: total occupied area, peak pressure location, center of pressure (CoP), and pressure gradient. A concentrated high-pressure blob near the front edge of the cushion suggests a small occupant sitting forward; a broad, low-pressure distribution suggests an adult reclined. The CoP is computed as a weighted centroid:

CoP_x = Σ(F_i · x_i) / ΣF_i
CoP_y = Σ(F_i · y_i) / ΣF_i

where F_i is the force at cell i and (x_i, y_i) its coordinates. A practical threshold used in many systems is a total force below roughly 30 N to declare the seat empty, and a CoP shift greater than 40 mm toward the front edge to flag a possible out-of-position (OOP) occupant.

Capacitive Sensors

Capacitive occupancy sensors place one or more electrodes in the seat cushion and measure the change in capacitance caused by the dielectric properties of whatever occupies the seat. A human body has a relative permittivity much higher than air, so the presence of a person increases the measured capacitance. The sensor front end typically drives the electrode with an AC signal and measures the resulting charge transfer.

The key advantage of capacitive sensing is its ability to distinguish a human from a wet seat or a bag of groceries, because the dielectric signature differs. A common implementation uses a guard electrode to shape the field and reduce sensitivity to nearby metal structures. The measured capacitance is converted to a classification state — empty, occupied by human, occupied by object — using thresholds calibrated per seat variant.

Capacitive sensors are fast (millisecond response) and low cost, but they are affected by humidity and by the occupant's clothing. A wet seat can mimic a human dielectric, so the fusion logic must cross-check with the pressure mat before declaring a human occupant.

Vision Systems

Vision-based OC uses a camera, typically mounted in the headliner or A-pillar, to capture the seat region. The image is processed to segment the occupant and estimate body landmarks: head position, shoulder height, torso angle, and knee position. Modern systems use a convolutional neural network (CNN) running on an embedded SoC to output a posture class and a bounding box.

Vision provides the richest information for OOP detection, which is critical because airbag deployment must be suppressed or derated when the occupant is too close to the instrument panel. A typical output is a distance estimate from the occupant's chest to the airbag module, with a threshold of about 250 mm used to trigger suppression. Vision also enables child seat detection by recognizing the seat shell geometry.

The limitations are environmental: direct sunlight, night driving, and occluding objects degrade performance. To mitigate this, vision is fused with the pressure mat, which remains reliable in darkness. The fusion logic uses vision for posture and position, and the pressure mat for presence and weight class.

Seat Belt Load Cells

A seat belt load cell is a strain-gauge or piezoelectric sensor integrated into the belt buckle or the anchor point. It measures the tensile force in the webbing, which correlates with the occupant's mass when the belt is properly worn. The signal is used to refine the weight classification and to detect whether the belt is routed correctly.

A typical load cell has a range of 0 to 500 N with a resolution of about 1 N. The OC algorithm integrates the belt force over the first few hundred milliseconds of a crash pulse to estimate occupant mass, then selects the appropriate inflator stage. If the belt force is near zero while the pressure mat indicates occupancy, the system flags an unbelted condition and adjusts the restraint strategy accordingly.

Fusion Architecture and Decision Logic

The four modalities are combined in a fusion ECU that runs a state machine. The following SVG illustrates the signal chain from sensors through feature extraction to the final classification output, including the cross-checks that resolve conflicts.

The fusion state machine resolves conflicts by priority. If the pressure mat reports force above the empty threshold and the capacitive sensor classifies the dielectric as human, the occupant is declared present. Vision then refines the posture class, and the belt load cell confirms the belted state. If the pressure mat and capacitive sensor disagree — for example, a heavy bag on a wet seat — the system defaults to the more conservative restraint mode, suppressing the airbag rather than risking an unnecessary deployment.

Practical Calibration and Verification

Calibration is performed per seat variant on a fixture that applies known loads at known positions. The pressure mat is calibrated with a 50 kg reference mass placed at the seat center; the capacitive sensor is calibrated empty and with a human subject; the vision system is calibrated with a target board at known distances. The belt load cell is calibrated with a dead weight of 100 N applied through the webbing.

Verification uses a set of test cases that cover the classification boundaries. A representative test matrix includes: empty seat, 5th percentile female, 50th percentile male, rear-facing child seat, forward-facing child seat, and an OOP occupant leaning forward. Each case is run at three temperatures (−30 °C, 23 °C, 65 °C) to expose drift. The pass criterion is that the classification output matches the expected class for all cases, with no more than one misclassification per 1000 cycles.

The sensing architecture described here provides the input that the restraint control unit uses to select deployment mode. The next chapter examines how that decision is translated into inflator staging and belt force limiting.