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| 1 | Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections显示文摘Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios. | Guofa Li Shenglong Li Shen Li Yechen Qin Dongpu Cao Xingda Qu Bo Cheng | 2020 | Automotive Innovation2020，3，4： | 4 |
| 2 | Differences in lower extremity muscular responses between successful and failed balance recovery after slips显示文摘 | Qu Xingda Hu Xinyao Lew Fuiling | 2012 | International Journal of Industrial Ergonomics2012，42，5： | 1 |
| 3 | Depth Estimation Based on Monocular Camera Sensors in Autonomous Vehicles: A Self‑supervised Learning Approach显示文摘Estimating depth from images captured by camera sensors is crucial for the advancement of autonomous driving technologies and has gained significant attention in recent years.However,most previous methods rely on stacked pooling or stride convolution to extract high-level features,which can limit network performance and lead to information redundancy.This paper proposes an improved bidirectional feature pyramid module(BiFPN)and a channel attention module(Seblock:squeeze and excitation)to address these issues in existing methods based on monocular camera sensor.The Seblock redistributes channel feature weights to enhance useful information,while the improved BiFPN facilitates efficient fusion of multi-scale features.The proposed method is in an end-to-end solution without any additional post-processing,resulting in efficient depth estimation.Experiment results show that the proposed method is competitive with state-of-the-art algorithms and preserves fine-grained texture of scene depth. | Guofa Li Xingyu Chi Xingda Qu | 2023 | Automotive Innovation2023，6，2： | 0 |
| 4 | ML-ANet:A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving显示文摘To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space(RKHS)so that the mean-embeddings of specific features in different domains could be precisely matched.Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching,which can further reduce domain discrepancy.Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet.The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments.These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios. | Guofa Li Zefeng Ji Yunlong Chang Shen Li Xingda Qu Dongpu Cao | 2021 | Chinese Journal of Mechanical Engineering2021，34，5： | 0 |
| 5 | On‑Ramp Merging for Highway Autonomous Driving:An Application of a New Safety Indicator in Deep Reinforcement Learning显示文摘This paper proposes an improved decision-making method based on deep reinforcement learning to address on-ramp merging challenges in highway autonomous driving.A novel safety indicator,time difference to merging(TDTM),is introduced,which is used in conjunction with the classic time to collision(TTC)indicator to evaluate driving safety and assist the merging vehicle in finding a suitable gap in traffic,thereby enhancing driving safety.The training of an autonomous driving agent is performed using the Deep Deterministic Policy Gradient(DDPG)algorithm.An action-masking mechanism is deployed to prevent unsafe actions during the policy exploration phase.The proposed DDPG+TDTM+TTC solution is tested in on-ramp merging scenarios with different driving speeds in SUMO and achieves a success rate of 99.96%without significantly impacting traffic efficiency on the main road.The results demonstrate that DDPG+TDTM+TTC achieved a higher on-ramp merging success rate of 99.96%compared to DDPG+TTC and DDPG. | Guofa Li Weiyan Zhou Siyan Lin Shen Li Xingda Qu | 2023 | Automotive Innovation2023，6，3： | 0 |
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