Function and Sensor KPIs: What’s the Difference?

This article focuses on two key types of KPIs: Function KPIs and Sensor KPIs. Understanding the difference between them and being able to support both are crucial for achieving a comprehensive and rigorous validation process for ADAS.

The clear definition of Function KPI and Sensor KPIs provides a more complete validation with better coverage to the manufacturer. It improves dependability and performance in ADAS features, paving the way for a safer driving experiences.

Power of Function: Assessing The Whole Picture

Function KPIs evaluate the performance of specific components or functions within an ADAS feature such as AEB, assessing how well these elements work together to accomplish the feature's intended purpose, beyond just the performance of individual sensors.

These KPIs include:

Accuracy: How often do the calculations that make up the feature lead to missed or false activations?

Processing Time: How fast does the sensor measurement translate into a system response?

Actuator Responses: Are the braking or steering actions correct in terms of timing, magnitude, and smoothness?

Think of Function KPIs as an orchestra conductor who ensures that all the musicians play together in harmony to create a beautiful performance. In the context of ADAS, these KPIs ensure that all components and functions of a feature work seamlessly together, resulting in enhanced safety and efficiency on the road.

Examples of Function KPIs:

Lane Departure Warning (LDW) System Response Time: Measures the time taken by the system to identify a lane departure and activate an appropriate alert. The response should be short enough to provide warning signals that the driver has time to react to.

Lane Keeping Assist System Stability: Evaluates the ability of the lane-keeping assist system to maintain the vehicle within its lane over varying road conditions and speeds. It measures the system's effectiveness in providing corrective steering inputs to prevent lane departure.?

Adaptive Cruise Control Responsiveness: This KPI assesses how quickly and accurately the adaptive cruise control system adjusts the vehicle's speed to maintain a safe following distance from the vehicle ahead. It includes evaluating the system's ability to detect changes in traffic flow and respond appropriately.

Traffic Sign Recognition System Accuracy: This KPI measures the precision with which the traffic sign recognition system identifies and interprets road signs. It evaluates the system's ability to provide correct and timely information to the driver, ensuring compliance with traffic regulations.

Blind Spot Detection System Reliability: This KPI assesses the reliability of the blind spot detection system in identifying vehicles in adjacent lanes. It includes measuring the system's ability to provide accurate alerts to the driver under various driving conditions, such as during lane changes or in heavy traffic.

Sensor Intelligence: Unveiling the Component Accuracy

Sensor KPIs delve into the complex performance details of individual sensor systems, which serve as the information providers to an ADAS system. These sensors are akin to a vehicle's sensory organs – its eyes and ears. The efficacy of these sensors directly impacts the overall effectiveness of ADAS functionalities.

Key Sensor KPIs include:

Accuracy: Score the accuracy of measurements taken by the sensor regarding distance, position, or other pertinent data.

Image Quality: For imaging sensors such as cameras and LiDARs, does the sensor system (including optics and soiling) provide sufficient fidelity for object detection?

Range: How far away can the sensor reliably detect objects?

Reliability: How consistently does the sensor function under different weather and light conditions?

Examples of Sensor KPIs:

Accuracy: This KPI measures the precision of the sensor's measurements regarding distance, position, or other critical data points. For example, a radar sensor should detect distances with an accuracy of ±0.1 meters.

Resolution: This KPI assesses the level of detail that imaging sensors, such as cameras and LiDARs, can capture. For instance, a camera sensor might be required to have a resolution of at least 1080p to ensure clear object detection and recognition.

Range: This KPI evaluates the maximum distance at which a sensor can reliably detect objects. An example could be a LiDAR sensor that should detect objects up to 200 meters away to provide sufficient time for the ADAS to respond.

Reliability: This KPI measures how consistently a sensor performs under various environmental conditions, such as different weather and lighting scenarios. For example, a sensor might need to maintain 99% reliability in detecting objects under both sunny and rainy conditions.