nach oben

International Journal of Automotive Technology

25.02.2025 | Connected Automated Vehicles and ITS, Vision and Sensors

DQN-Based Automatic Emergency Collision Avoidance Control Considering Driver Style

verfasst von: Xiaohui Lu, Pengfei Zhang, Xinyi Zheng, Ruixia Xiong, Niaona Zhang, Shaosong Li

Erschienen in: International Journal of Automotive Technology

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, a deep Q-network (DQN) longitudinal collision avoidance control method is proposed to achieve excellent safety, while improve the driving experience by considering driver style. In terms of network structure, the driver style information is concatenated with image and motion information between convolutional and fully connected neural networks, achieving the fusion of information from different dimensions. In addition, the ideal safe distance is designed into the reward function to achieve end-to-end longitudinal collision avoidance control considering driver style. A simulation experimental environment is established based on Carla to test the control effects. The simulation results show that the control strategy of proposed method has a considerable effect on handling longitudinal collision avoidance problems. The longitudinal collision avoidance control of the vehicle exhibits distinct variations under different driver styles, and the longitudinal collision avoidance process can meet the psychological expectation of drivers.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information.

Order your 30-days-trial for free and without any commitment.

Jetzt informieren

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik.

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

Jetzt informieren

Alvarez, L., Yi , J., Horowitz, R. Olmos, L. (2000). Emergency braking control in automated highway systems with underestimation of friction coefficient. In: Proceedings of the 2000 American Control Conference. (ACC) 1, 6, 574–579.

Chen, J., Li, S. E., & Tomizuka, M. (2021). Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5068–5078.CrossRefMATH

Desjardins, C., & Chaib-Draa, B. (2011). Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1248–1260.CrossRefMATH

Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2020). A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(6), 5876–5888.CrossRefMATH

Guo, H. J., Li, C. W., Luo, G. Y., Chen, T., & Li, K. (2021). Driver Car-following model based on deep reinforcement learning. Automotive Engineering, 43, 571–579.MATH

Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., Lu, L., Jia, X., Liu, Q., Dai, J., Qiao, Y., Li, H. (2023). Planning-oriented autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17853–17862.

Ji, J., Khajepour, A., Melek, W. W., & Huang, Y. (2016). Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Transactions on Vehicular Technology, 66(2), 952–964.CrossRefMATH

Kim, M., Lee, S., Lim, J., Choi, J., & Kang, S. G. (2020). Unexpected collision avoidance driving strategy using deep reinforcement learning. IEEE Access, 8, 17243–17252.CrossRef

Koutník, J., Schmidhuber, J., Gomez, F. (2014). Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation, 541–548.

Li, J., Yao, L., Xu, X., Cheng, B., & Ren, J. (2020). Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving. Information Sciences, 532, 110–124.CrossRefMATH

Li, W. L., Zhang, X. S., Han, D., Qian, H., & Shi, X. H. (2021). Vehicle autonomous collision avoidance decision control model based on deep reinforcement learning. Journal of Automotive Safety and Energy, 12(2), 201.MATH

Liu, Z., Cao, S. P., Shen, Y., & Yang, X. (2020). Signal control of single intersection based on improved deep reinforcement learning method. Comput Sci (in Chinese), 47(12), 226–232.MATH

Liu, Y. C., Liu, H. Y., Luo, X., et al. (2022). Trajectory optimization of connected vehicles at isolated intersection in mixed traffic environment. Journal of Transportation Systems Engineering and Information Technology, 22(2), 154.MATH

Ma, Z., & Zhang, Y. (2021). Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver’s driving styles. Analysis and Prevention, 159(4), 106238.CrossRefMATH

Martin, R., Kusev, I., Cooke, A. J., Baranova, V., Van Schaik, P., & Kusev, P. (2017). Commentary: The social dilemma of autonomous vehicles. Frontiers in Psychology, 8, 808.CrossRef

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.CrossRef

Murphey, Y. L., Milton, R. and Kiliaris, L. (2009). Driver’s style classification using jerk analysis. In: 2009 IEEE workshop on computational intelligence in vehicles and vehicular systems. IEEE 23–28.

Nasr, V., Wozniak, D., Shahini, F., & Zahabi, M. (2021). Application of advanced driver-assistance systems in police vehicles. Transportation Research Record, 2675(10), 1453–1468.CrossRefMATH

Park, J., Kim, D., & Huh, K. (2021). Emergency collision avoidance by steering in critical situations. International Journal of Automotive Technology, 22, 173–184.CrossRefMATH

Park, J. M., Kim, D. W., Yoon, Y. S., Kim, H. J., & Yi, K. S. (2009). Obstacle avoidance of autonomous vehicles based on model predictive control. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 223(12), 1499–1516.MATH

Peiris, S., Newstead, S., Berecki-Gisolf, J., Chen, B., & Fildes, B. (2022). Quantifying the lost safety benefits of ADAS technologies due to inadequate supporting road infrastructure. Sustainability, 14(4), 2234.CrossRef

Qin, C., Wang, Y., Zhang, Y., & Yin, C. (2023). Bird’s-eye view semantic segmentation and voxel semantic segmentation based on frustum voxel modeling and monocular camera. Journal of Shanghai Jiaotong University (Science), 28(1), 100–113.CrossRefMATH

Sagberg, F., Selpi, Piccinini, G. F. B., & Engström, J. (2015). A review of research on driving styles and road safety. The Journal of the Human Factors Society, 57(7), 1248–1275.CrossRefMATH

Zhang, L., Wang, J., & Li, K. (2006). A study on collision warning/ avoidance algorithms based on driver’s behaviors during car-following. Automotive Engineering, 28(4), 351–375.MATH

Zhang, Y. Z., Wang, Y. C., Zhao, W., & Feng, J. (2022). Longitudinal and lateral collision avoidance control strategy for intelligent vehicles. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 236(2), 268–286.CrossRefMATH

Zhao, J., Shi, J., & Zhuo, L. (2024). BEV perception for autonomous driving: State of the art and future perspectives. Expert Systems with Applications, 258, 125103.CrossRefMATH

Zheng, G., Zu, Z., & Kong, Z. (2021). The collision avoidance strategy of automatic emergency braking system considering the response time of the driver. Journal of Chongqing University of Technology (Natural Science), 34(12), 45–52.MATH

Titel: DQN-Based Automatic Emergency Collision Avoidance Control Considering Driver Style
verfasst von: Xiaohui Lu
Pengfei Zhang
Xinyi Zheng
Ruixia Xiong
Niaona Zhang
Shaosong Li
Publikationsdatum: 25.02.2025
Verlag: The Korean Society of Automotive Engineers
Erschienen in: International Journal of Automotive Technology
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI: https://doi.org/10.1007/s12239-025-00224-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

ATZelectronics worldwide

ATZelektronik