A lightweight deep learning model for real-time obstacle avoidance in autonomous vehicles

Key Points

• Obstacle avoidance performance improved with the lightweight deep learning model, achieving lower latency than traditional methods.
• The model processes data in real-time, enabling immediate responses to obstacles detected on the road.
• Utilizing a deep learning approach, the model enhances navigation safety for autonomous vehicles at varying speeds.
• This work underscores the necessity for efficient real-time algorithms to improve the safety and effectiveness of autonomous driving.