Active Safety

Active safety research focuses on developing systems to prevent vehicle accidents by detecting and mitigating hazardous situations in real-time. Current efforts concentrate on improving the accuracy and reliability of driver behavior prediction (using models like convolutional neural networks and Gaussian processes), object detection (leveraging YOLO and other deep learning architectures), and automated decision-making (employing LLMs for streamlined software release processes). These advancements aim to enhance the effectiveness of existing active safety systems, such as collision avoidance and driver assistance technologies, ultimately leading to safer roads and improved vehicle safety performance.