Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network
Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/24/8498 |
| _version_ | 1827669612268879872 |
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| author | Lei Yang Chunqing Zhao Chao Lu Lianzhen Wei Jianwei Gong |
| author_facet | Lei Yang Chunqing Zhao Chao Lu Lianzhen Wei Jianwei Gong |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness. |
| first_indexed | 2024-03-10T03:05:39Z |
| format | Article |
| id | doaj.art-4c603edabfc04ac8b98072f51506988a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:05:39Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4c603edabfc04ac8b98072f51506988a2023-11-23T10:32:20ZengMDPI AGSensors1424-82202021-12-012124849810.3390/s21248498Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief NetworkLei Yang0Chunqing Zhao1Chao Lu2Lianzhen Wei3Jianwei Gong4School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaChina North Vehicle Research Institute, Beijing 100072, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaAccurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.https://www.mdpi.com/1424-8220/21/24/8498driving behavior predictiondeep belief networkintelligent vehicles |
| spellingShingle | Lei Yang Chunqing Zhao Chao Lu Lianzhen Wei Jianwei Gong Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network Sensors driving behavior prediction deep belief network intelligent vehicles |
| title | Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network |
| title_full | Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network |
| title_fullStr | Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network |
| title_full_unstemmed | Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network |
| title_short | Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network |
| title_sort | lateral and longitudinal driving behavior prediction based on improved deep belief network |
| topic | driving behavior prediction deep belief network intelligent vehicles |
| url | https://www.mdpi.com/1424-8220/21/24/8498 |
| work_keys_str_mv | AT leiyang lateralandlongitudinaldrivingbehaviorpredictionbasedonimproveddeepbeliefnetwork AT chunqingzhao lateralandlongitudinaldrivingbehaviorpredictionbasedonimproveddeepbeliefnetwork AT chaolu lateralandlongitudinaldrivingbehaviorpredictionbasedonimproveddeepbeliefnetwork AT lianzhenwei lateralandlongitudinaldrivingbehaviorpredictionbasedonimproveddeepbeliefnetwork AT jianweigong lateralandlongitudinaldrivingbehaviorpredictionbasedonimproveddeepbeliefnetwork |