ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 16, No. 04, Month DECEMBER, Year 2022, Pages 393 - 409
Mfpe : a loss function based on multi-task autonomous driving
Youwei Li, Jian Qu
Abstract Download PDFRoad tracking, traffic sign recognition, obstacle avoidance, and real-time acceleration and deceleration are some critical sub-tasks in autonomous driving. This research proposed to use a single-sensor (camera) based intelligent driving platform to achieve multi-task (four subtasks) autonomous driving. We adjusted the function combinations and hyperparameters of the model to improve the model training and model testing performance. The experiments showed that the existing function combinations could not significantly improve the autonomous driving performance, and the loss function had a significant impact on the autonomous driving performance of the model. Therefore, we designed a novel loss function (MFPE) based on multi-task autonomous driving. The models with the MFPE loss function outperformed the original and existing models in model training and actual multi-task autonomous driving performance. Meanwhile, the model with the MFPE loss function achieved multi-task autonomous driving under different lighting conditions, untrained routes, and different static obstacles, which indicates that the MFPE loss function enhances the robustness of the model. In addition, the speed of the intelligent driving platform can reach up to 5.4 Km/h.
Convolutional Neural Network, Recurrent Neural Network, Deep learning, Long Short-Term Memory, MFPE Loss Function, Multi- task Autonomous Driving