Application of KPIs to Driver Assisted Systems

The ability to distinguish between System, Feature, Function, and Sensor KPIs allows manufacturers to implement a more comprehensive and thorough validation process. This approach improves the reliability and performance of ADAS features, making driving safer, easier, and more efficient. Keeping track of these different tiers of KPIs—from the system level down to sensor specifics—plays an essential role in the development, validation, and maintenance processes of effective ADAS systems. This impacts vehicle safety as well as user experience.

Adaptive Cruise Control (ACC)

ACC systems maintain a safe distance from the vehicle ahead by adjusting the speed.

System KPIs

System Reliability: Measures the uptime and failure rates.

Example: ACC should have a mean time between failures (MTBF) of at least 10,000 hours.

User Acceptance: Evaluates user satisfaction.

Example: 95% of users should find the ACC intuitive and reliable.

Feature KPIs

ACC Accuracy: Measures the precision in maintaining a set following distance.

Example: ACC should maintain a following distance within ±0.5 meters of the set distance.

ACC Response Time: Evaluates the time taken to adjust speed.

Example: ACC should adjust the vehicle's speed within 1 second of detecting a change in the lead vehicle's speed.

ACC Smoothness: Ensures a smooth driving experience without abrupt changes.

Example: ACC should adjust speed smoothly, avoiding sudden acceleration or deceleration.

Function KPIs

Radar and Camera Data Fusion: Assesses the effectiveness of integrating radar and camera data to improve detection accuracy.

Example: Integrated data should reduce false positives by 10%.

Actuator Response Precision: Evaluates the precision of speed adjustments made by the vehicle's actuators.

Example: Speed adjustments should be within ±1 km/h of the target speed.

Sensor KPIs

Radar Reliability: Tracks radar performance under varying conditions.

Example: Radar should maintain 99% reliability in rain.

Camera Response Time: Measures the time to capture and process images.

Example: Camera should have a response time of less than 10 milliseconds.

Radar Accuracy: Ensures precise distance measurements for safe following.

Example: The radar sensor should detect distances with an accuracy of ±0.1 meters.

Automatic Emergency Braking (AEB)

AEB systems are designed to prevent collisions or mitigate their severity by automatically applying the brakes when a potential collision is detected.

System KPIs

System Reliability: Ensures high reliability in emergency situations.

Example: AEB should have a mean time between failures (MTBF) of at least 10,000 hours.

Overall Safety Impact: Measures the reduction in collision rates.

Example: Vehicles with AEB should show a 30% reduction in rear-end collisions.

Feature KPIs

AEB Activation Time: Measures the time from threat detection to brake activation.

Example: AEB should activate within 0.3 seconds of detecting an imminent collision.

AEB Efficiency: Evaluates the stopping distance from a set speed.

Example: AEB should bring the vehicle to a complete stop within 10 meters from 50 mph.

Function KPIs

Lidar and Radar Integration: Assesses the accuracy of object detection using combined sensor data.

Example: Integrated sensor data should detect objects with a precision of ±0.1 meters.

Brake Actuation Precision: Evaluates the precision and smoothness of brake application.

Example: Brake actuation should achieve the desired deceleration within ±0.1 m/s2.

Sensor KPIs

Radar Reliability: Consistent performance in various conditions.

Example: Radar should maintain 99% reliability under different weather conditions.

Lidar Precision: Measures the accuracy of distance measurements.

Example: Lidar should detect distances with a precision of ±0.05 meters.

Lane Keeping Assist System (LKAS)

LKAS helps drivers stay within their lane by providing steering assistance.

System KPIs

Lane Keeping Accuracy: Measures the system's ability to keep the vehicle centered in the lane.

Example: LKAS should maintain the vehicle within ±10 cm of the lane center.

System Availability: Measures the percentage of time LKAS is operational.

Example: LKAS should be available 95% of the time during highway driving.

Feature KPIs

LKAS Response Time: Evaluates how quickly the system responds to lane departure.

Example: LKAS should respond within 0.5 seconds of detecting lane departure.

Steering Assistance Precision: Measures the accuracy of the steering corrections.

Example: Steering corrections should be within ±2 degrees of the required angle.

Function KPIs

Camera and Lidar Integration: Evaluates the precision of lane marking detection using combined sensor data.

Example: Integrated sensor data should detect lane markings with an accuracy of ±0.1 meters.

Sensor KPIs

Camera Field of View: Measures the angular range of the camera.

Example: Camera should have a field of view covering 120 degrees.

Sensor Calibration Stability: Assesses how long sensors maintain calibration.

Example: Sensors should maintain calibration for at least 10,000 km.