Sensors

Vehicles use 100+ sensors. Learn position and speed sensors (Hall, VR, crank/cam, wheel speed), pressure (MAP, brake, tyre), temperature (NTC thermistors), flow (MAF), oxygen/λ sensors, inertial sensors, and ADAS sensors (radar, camera, lidar, ultrasonic) — with signal conditioning and diagnostics.

Every control decision begins with a measurement: crank angle for fuel injection, wheel speed for ABS, yaw rate for stability control, temperature for thermal protection, radar distance for automatic braking.

Automotive sensors must be accurate, robust, cheap and diagnosable — because wrong data leads to wrong decisions.

An analogy

Sensors are the car's senses — eyes (cameras), ears (ultrasonic), balance (IMU), touch (pressure) — and the ECUs are the brain interpreting them.

Common sensor types

Position/speed: Hall-effect and variable-reluctance (crank, cam, wheel speed), potentiometers and contactless angle sensors (pedal, throttle, steering). Pressure: piezoresistive MEMS (MAP, brake, tyre pressure). Temperature: NTC thermistors. Flow: hot-film MAF. Exhaust: zirconia λ sensors, NOx sensors.

Signal conditioning

Analog sensors feed dividers/amplifiers into ADCs; frequency sensors feed timer capture inputs; smart sensors send digital data (SENT, PSI5, SPI, CAN). Filtering removes noise; plausibility checks detect faults (out-of-range, stuck, implausible rate).

Inertial and chassis sensors

MEMS accelerometers and gyroscopes (IMUs) for airbags, ESC and navigation; steering-angle and torque sensors for EPS; ride-height sensors for headlamp levelling and air suspension.

ADAS sensors and fusion

Cameras (lanes, signs, objects), 77 GHz radar (range and relative speed, all-weather), lidar (3D point clouds), ultrasonic (parking). Each has strengths and weaknesses; sensor fusion combines them for robust perception. Calibration after windscreen replacement or collision is essential.

Formulas

• NTC thermistor (β model): R_T = R₂₅ · exp[β(1/T − 1/298.15)]
• Wheel-speed frequency: f = N_t · v / (2π r)
• Engine speed from crank wheel: N (rpm) = 60 f / N_teeth
• Radar range: R = c · Δt / 2

Worked example: Coolant sensor resistance

Given: NTC R₂₅ = 10 kΩ, β = 3950 K; T = 90 °C

Find: Resistance and divider voltage (10 kΩ pull-up, 5 V).

• R = 10 000 × exp[3950(1/363.15 − 1/298.15)] = 934 Ω
• V = 5 × 934/(934 + 10 000) = 0.43 V

Answer: ≈ 934 Ω, ≈ 0.43 V

Worked example: Wheel-speed sensor frequency

Given: 48-tooth tone wheel, tyre radius 0.31 m; 100 km/h

Find: Signal frequency.

• Wheel speed = 27.78/(2π × 0.31) = 14.26 rev/s
• f = 48 × 14.26 = 685 Hz

Answer: ≈ 685 Hz

Common mistakes

• Wrong: Linear conversion of NTC voltage. — Right: Use the β/Steinhart–Hart equation or calibrated lookup tables.
• Wrong: No plausibility checks. — Right: Detect open/short, stuck and implausible values for diagnostics and safety.

Applications

• Engine & emissions: Crank/cam, MAP/MAF, λ and NOx sensors.
• Chassis & safety: Wheel speed (ABS), IMU (ESC, airbags), steering angle.
• ADAS: Radar, camera, lidar and ultrasonic arrays.

Key takeaways

• Position/speed, pressure, temperature, flow, exhaust, inertial, ADAS.
• NTC: R = R₂₅ exp[β(1/T − 1/298)].
• Wheel-speed f = N_t v/(2πr).
• Condition, filter and plausibility-check every signal.

Going further

• Sensor fusion: Kalman filters and neural networks combine heterogeneous sensors.
• Virtual sensors: Models estimate unmeasured quantities (e.g. tyre friction, battery core temperature).
• Redundancy: Safety-critical signals (pedal, steering torque) use dual sensors with cross-checks.