Dozens of sensors placed throughout today’s cars collect a trove of data about everything from acceleration to engine control, pressure, temperature, and velocity. To react instantaneously and appropriately, the data must be processed swiftly, with intelligence providing guidance on the right actions. For example, consider an application like an advanced driver assistance system (ADAS). The combination of radar plus cameras and LiDAR serves as the eyes on the road for ADAS. The higher degree of autonomy in a vehicle (Level 2 and above), the more critical it becomes for radar sensors to collect a wider range of data to provide a full surround view of the car. AI algorithms applied on the data collected by these sensors derive insights that direct the vehicle’s response, such as delineating between a person or an empty box in the middle of the street and braking or swerving accordingly. Another important consideration with radar is the presence of interference. Increasing prevalence of radar systems also increases the possibility of interference, which can be mitigated with a processor that can detect and correct these problems.
Another example where AI plays a useful role is in optimizing battery performance in electric vehicles. AI can predict the battery condition of subset cells and, through neural network implementations, reduce the current sensors in the on-board charging units. Similarly, AI technology can be used to predict the aging behavior of lithium-ion batteries and, thus, help optimize their performance. For instance, Porsche Engineering has been doing so, tapping into data points including temperature, the battery state-of-charge, and the internal resistance of the battery. Its AI technology has been trained to adapt to driver profiles, making the predictions more precise over time.
Even though they are small, physical sensors still take up valuable real estate in a vehicle. And with more sensors needed to support increasingly autonomous functions, the ability to replace some of these physical devices with virtual ones, enhanced with AI, can be a welcome opportunity. Through the use of artificial neural networks (ANNs) and a Kalman filter (an estimation algorithm) in system controllers, virtual sensors can take the place of their physical counterparts. The virtual sensors would be driven through predictive modeling of an actuator or via the use of ANNs and state-space-based observers, maintaining required safety levels while lowering costs.