The development of automotive perception systems is a complex process, requiring extensive data to adequately train them. rFpro spoke with Martin Punke, Head of Camera Sensor Technology at Continental, about the role of simulation in sensor hardware development and its impact on training computer vision algorithms for ADAS and automated driving.
rFrpo: First of all, can you tell us a little bit about your role at Continental?
MP: My team is responsible for the camera hardware, specifically the optoelectronics. We handle lens selection, image sensor selection, and overall image quality testing and tuning. A significant part of our work involves sensor modelling. We are trying to mimic the optoelectronic behaviour of our cameras as accurately as possible in simulation environments.
rFpro: What are the biggest challenges in developing camera hardware for perception systems?
MP: One of the key challenges is determining how good the hardware needs to be to meet system requirements. For example, if a vehicle must detect a traffic sign at 200 meters, we need to define the necessary image resolution and optical performance. This challenge is compounded by the increasing complexity of computer vision algorithms and neural networks, making it difficult to pinpoint exact hardware specifications without extensive validation.
Another major challenge is cost. The industry is always looking for ways to optimise hardware, potentially reducing costs while still meeting performance criteria. We also need to consider real-world variability to ensure the system functions reliably across different lighting conditions and weather.
rFpro: How does simulation help solve these challenges?
MP: Traditionally, camera performance is evaluated by physically building the hardware and testing it in real-world scenarios. However, this approach is costly and time-consuming. Simulation allows us to predict performance before hardware is built, helping us refine our designs earlier in the development process.
Additionally, real-world testing has limitations. Some critical scenarios, such as emergency braking for a pedestrian, are difficult or unsafe to test in physical environments. Simulation enables us to create and test these edge cases under controlled conditions, ensuring that our systems can handle them reliably.
rFpro: What role does sensor modelling play in improving perception systems?
MP: Sensor modelling is essential because it allows us to replicate the real-world behavior of our cameras in a virtual environment. We create detailed models of our camera hardware, including lenses and image sensors, to simulate how they interact with different lighting conditions, weather, and motion dynamics. This helps us refine our designs and predict performance before manufacturing physical prototypes.
A key aspect of sensor modelling is accounting for optical distortions, motion blur, and rolling shutter effects. If these factors aren’t accurately simulated, the perception system may not perform as expected when deployed in real vehicles. By improving sensor modelling, we can create training data that better represents real-world conditions, leading to more robust and reliable algorithms.
rFpro: What is the process of creating a sensor model and what are the challenges?
MP: Sensor modelling starts with optics – ensuring the lens and image sensor behave as they would in real life. This process is challenging because optical effects involve complex interactions, not just with the lens but also with elements like the vehicle’s windshield.
Then, we must account for digital image sensor behaviour. Motion blur, rolling shutter effects, and other dynamic factors significantly impact image quality. These effects can’t be easily applied post-rendering; they need to be integrated into the simulation engine itself.
rFpro: How important is variation in simulation data?
MP: Variation is critical. In simple terms, if an algorithm is only trained on one type of object, let’s say, red cars, it might struggle when encountering a blue van for example. This problem, known as overfitting, can lead to unreliable performance in diverse environments.
Simulation allows us to introduce a wide range of variations, such as different vehicle types and colours, lighting conditions, road surfaces, and even minor imperfections like road marking inconsistencies. This ensures that the perception system can perform in a broad environment rather than being tuned to a narrow dataset.
rFpro: Why is realism in simulation so important?
MP: For driving functions, the accuracy of the simulation is less critical, but it is very important for detection functions. The more realistic the simulation is, the more accurate the data is, the more reliable the trained algorithm will be in the real world. Physically modelled rendering ensures that the simulated camera data closely mimics how a real sensor perceives the world. This includes factors like motion blur, shutter effects, and lens distortions. If these elements are missing, the images produced are likely to be “over-sharp”, which is not representative of the real world.
rFpro: What are the risks of training perception systems on unrealistic data?
MP: If an algorithm is trained on overly sharp, perfect images, it won’t perform well in real-world conditions where motion blur, lighting variations, and environmental factors introduce imperfections. This mismatch can lead to poor detection performance and safety risks when deployed in a vehicle.
