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Creating safe and robust autonomous driving (AD) systems is a complex task. Autonomous vehicles (AVs) have hundreds of sensors, all of which need to work with one another inside the car and with other smart vehicles. The software algorithms enabling autonomous driving features will ultimately need to synthesize all the information collected from these sensors to ensure that the vehicle responds appropriately. These algorithms require testing against millions of complex scenes covering various driving scenarios.
Achieving the next level of vehicle autonomy will require many innovations and technological advancements. Continuous investments in sensor technologies such as radar, lidar, and cameras will improve environmental scanning. Each sensor type has its advantages and disadvantages and they need to complement each other to ensure that the object detection process has built-in redundancy.
Software is driving the development trends and topics, such as autonomous driving and electrification. The focus of vehicle development is therefore shifting from hardware to software. Vehicles with ADAS Levels 3 and beyond require testing and validation against the growing number of scenarios and the surrounding environment. Not only will the number of tests increase, but the complexity of the tests will also increase.
With the technological advancements and changing development trends, R&D engineers need to reimagine test tactics for ADAS and AV systems. This white paper provides insights and key steps needed to sign off on new ADAS and AV functionality confidently. Download the white paper to find out how you can:
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