Behavioral Cloning Models Reality Check for Autonomous Driving
This provides real-world validation for autonomous driving perception systems, though it is incremental as it applies existing methods to new data.
The paper tackled the lack of real-world validation for vision-based autonomous vehicle control systems by testing state-of-the-art Behavior Cloning models on a scaled vehicle, showing they predict steering angles with low error margins in real-time.
How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications.