End-To-End Autonomous Vehicle Navigation Using Imitation Learning

The proposed model is a novel deep learning model that combines end-to-end and multi-task learning approaches for point-to-point navigation in autonomous driving. Our model simultaneously performs perception and control tasks to ensure safe and reliable navigation. The perception module of the model takes high-dimensional observations from an RGBD camera and performs tasks such as semantic segmentation, semantic depth cloud (SDC) mapping, and predicting traffic light states and stop signs. The encoded features, along with GPS and speedometer information, are then decoded by the control module to predict waypoints in a latent feature space. Two agents process these outputs and generate a control policy that determines the appropriate steering, throttle, and brake actions. The model is evaluated using the CARLA simulator, considering various scenarios including normal and adversarial situations, as well as different weather conditions to simulate real-world driving conditions. The model’s performances is compared with recent models in terms of driving score and conduct an ablation study on SDC mapping and multi-agent approaches to understand their impact. The model achieves the highest driving score even with fewer parameters and computational load.