Enhancing Vehicle Safety through ADAS Verification and Validation

The world around us is evolving, with technologies changing with the blink of an eye. In the automotive world, focus has shifted to enhance technologies to increase the safety and user-experience (UX) for vehicle owners. Vehicles are now equipped with Advanced Driver Assistance Systems (ADAS), which can assist drivers in case of potential dangers. ADAS uses Human-machine Interface to improve the driver's ability to react to dangers on the road. ADAS has enhanced vehicle safety by providing drivers with assistance in various situations, including collision avoidance and automatic parking to adaptive cruise control and lane departure warnings. These systems provide additional safety as they increase reaction times to potential through early warning and automated systems.

The integration of ADAS in passenger vehicles — specifically cars, trucks, vans, and SUVs — is estimated to have the potential to prevent 40% of all passenger-vehicle crashes, 37% of injuries that occur in passenger-vehicle crashes, and 29% of deaths in crashes involving passenger vehicles.

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Need for ADAS Verification and Validation (VnV)

It is estimated by McKinsey that every passenger car will come with some level of ADAS built-in it by 2030. Around 12 percent of new passenger cars sold will come with L3+ autonomous technologies, and 37 percent will have advanced ADAS technologies by 2035. ADAS is a combination of multiple sensors, actuators, and algorithms working together in harmony to perform functions such as cruise control, lane-keeping assistance, automatic emergency braking, and blind-spot monitoring. The demand for such sophisticated technology increases the need for testing several intelligent features related to ADAS.

As more and more ADAS features get introduced in the vehicle, buyers become more reliant on them and expect that these technologies work flawlessly. Thus, ADAS should be competent enough to handle potential risks and obstacles in a variety of real-world circumstances. To know that these systems are working effectively, they go through rigorous testing and validation methods before launching the vehicles on the road

What is Verification and Validation?

Verification is testing, quality auditing, and expert reviewing of the system and its supporting software to ensure that the system is designed to specification. Whereas validation is the process that confirms that the end-product will function as intended and has met all the safety goals.

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Several governing bodies have defined standards and guidelines for vehicle manufacturers to follow in verifying and validating ADAS features. These governing bodies consist of ISO 26262, Euro NCAP, NHTSA Guidelines, SAE J3016, and others. These governing bodies and standards give risk assessment concepts such as Hazard Analysis and Risk Assessment (HARA), which in turn helps in determining the level of Safety Integrity. The level of integrity defined by HARA is known as Automotive Safety Integrity Levels (ASILs). ASILs establish the level of rigor required in each step for development, testing, and production.

But these tests cannot cover all scenarios for diverse road conditions and vehicle behavior. So different simulated conditions are introduced for testing and validation of ADAS systems. They include lab simulations such as Hardware in the Loop, Software in the Loop, and Driver in the Loop which can provide the means to cover a high number of situations and is cost-effective at the same time.

Type of Tests for Verification and Validation

A common way to test the vehicle is to hold test drives in different places like highways, cities, or special tracks. This type of test drive is helpful as it can measure the vehicles' performance in real-world situations. Here’s where autonomous vehicles are different; it is extremely risky to measure collision avoidance on the road, and it's almost impossible to cover the unexpected scenarios that the vehicle can get into. To add to this real-world testing is time-consuming and high in cost not to mention dangerous to everyone, especially to the driver. Hence, personal real-test drive is the last step in the ADAS verification and validation cycle.

Virtual Simulation: Creating a virtual scenario using software for ADAS systems including the driver, sensors, and traffic is much safer than real-life testing. This also decreases the development cost and aids in prototyping and developing new system features.

Model-In-Loop (MIL): MIL testing is a process used in model-based development to evaluate the performance of a model, that is developing the actual model in a simulated environment. MIL testing helps engineers understand the behavior of a model under certain conditions and identify areas where improvements can be made.

Software-in-Loop (SIL): SIL tests components of ADAS software by linking the algorithms that correspond to certain vehicle’s hardware to the simulation. With SIL, developers can check the performance of code in a simulated environment without actual hardware parts.

Hardware-in-the-Loop (HIL): HIL, traditionally a tool for developing a car’s engine and vehicle dynamic controllers, is now gaining traction as a method for ADAS and autonomous car testing. It checks the vehicle hardware performance in a real-world environment, which is excellent for prototyping.

Drive-in-the-Loop (DIL): DIL is a process where real people drive in simulated vehicles having controls and everything similar to real vehicles in a simulated environment. This aids in getting input from human drivers which can be crucial even at the time of development of ADAS.

Vehicle-Hardware-in-the-Loop (VEHIL): VEHIL is a multi-agent simulator that includes real autonomous vehicles and several artificial robotic platforms working in the lab. In this method, actual collision avoidance tests can take place as it checks the vehicle’s performance with targets that simulate other vehicles on the road.

Vehicle-in-Loop (VIL): In the VIL method, a human driver and a real autonomous vehicle operate in a simulated environment. This approach aids in understanding human behavior inside autonomous vehicles, for instance, reaction time, warning system evaluation, etc.

Augmentation of Measurement Data: This method is useful for testing autonomous vehicle perception systems as it is based on real video sequences from test drives. This process gives developers real and virtual data to improve car perception.

Goken’s contribution to Verification and Validation for ADAS Systems

As a premier engineering services firm, Goken stands at the forefront of the automotive industry, offering unparalleled support for the verification and validation of Advanced Driver Assistance Systems (ADAS). With global presence across America, India, and Japan, our team of engineers brings  expertise in every project.

Goken's technical prowess is backed by a deep understanding of programming languages essential for ADAS development, including C++, C, C#, Embedded C, CAPL, and Python. Our proficiency with programming tools such as DOORS, Vector CANoe, CANalyzer and dSPACE. Goken's engineers are adept at designing simulation tools with AutoCAD, Matlab, Simulink, and LabVIEW, ensuring that every aspect of ADAS is meticulously tested.

In verification and validation, Goken's expertise lies in both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) testing, capable of creating and executing comprehensive test plans and test cases. We leverage data analysis and interpretation to validate ADAS system performance, ensuring that every system meets the rigorous safety standards of the automotive industry.