AI-Based Solutions for Vehicle Safety System Optimization

Abstract

The integration of artificial intelligence (AI) solutions in a vehicle safety system can significantly improve response time and decision-making. AI-based systems can minimize human error and, most importantly, reduce the probability of an accident. Indeed, any vehicle safety system is built of several subsystems that work in synergy to (1) prevent or avoid an accident by optimizing the driver’s performance through issuing warnings and (2) mitigate the severity of an accident when such systems fail to predict and prevent it. AI can significantly affect the performance of such systems. One of the fastest-growing applications of AI in vehicle safety systems and telematics is the accident avoidance and mitigation systems.

Early examples of AI-based automotive applications include those developed and tested in the late 1960s and early 1970s. These “smart” vehicles implemented complex “reasoning” software to analyze the preceding moving obstacle. Indeed, an AI-based system is an ideal tool to enable precision monitoring of real-time information, and analyzing that information helps provide real-time warnings to the driver or take over control to maintain safety. As a result, AI-based solutions can add great value to many automotive safety features, particularly for real-time and time-critical operations such as collision warning or avoidance; lane change warnings and assistance; and other warnings that rely on forward or side-looking radars and cameras to monitor external surroundings in these complex systems. In modern vehicles, complex and interconnected subsystems monitor various driving and crash values and internally communicate to achieve the required safety feature.