Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we propose a novel deep learning model trained with end-to-end and multi-task learning manners to perform both perception and control tasks simultaneously. The model is used to drive the ego vehicle safely by following a sequence of routes defined by the global planner. The perception part of the model is used to encode high-dimensional observation data provided by an RGBD camera while performing semantic segmentation, semantic depth cloud (SDC) mapping, and traffic light state and stop sign prediction. Then, the control part decodes the encoded features along with additional information provided by GPS and speedometer to predict waypoints that come with a latent feature space. Furthermore, two agents are employed to process these outputs and make a control policy that determines the level of steering, throttle, and brake as the final action. The model is evaluated on CARLA simulator with various scenarios made of normal-adversarial situations and different weathers to mimic real-world conditions. In addition, we do a comparative study with some recent models to justify the performance in multiple aspects of driving. Moreover, we also conduct an ablation study on SDC mapping and multi-agent to understand their roles and behavior. As a result, our model achieves the highest driving score even with fewer parameters and computation load. To support future studies, we share our codes at https://github.com/oskarnatan/end-to-end-driving.
翻译:侧重于自主驱动器的点对点导航任务,我们提出一个新的深层次学习模式,该模式经过端到端和多任务学习方式的培训,以同时执行感知和控制任务。该模式用于按照全球规划员确定的一系列路线安全驱动自我驱动飞行器。该模式的感知部分用于编码由RGBD相机提供的高维观测数据,同时进行语义分解、语义深度云(SDC)绘图、交通灯光状态和停止信号预测。然后,该控制部分解码了编码特征,以及由全球定位系统和速度计提供的额外信息,以预测带有潜在特征空间的路径。此外,使用两个代理商处理这些输出并制定一项控制政策,确定方向、节流和刹作为最后行动的水平。该模型在CARLA模拟器上进行了评估,其各种情景为正常对抗状态/天气提供了支持,以及模拟现实世界状况。此外,我们做了一项比较研究,与一些最近的一些模型进行了编码解码,以证明我们未来驱动力模型的多方面表现。此外,我们还进行了一个甚至理解了Sdrimalal的进度分析。