Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach
A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can caus...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10510416/ |
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| author | Ying-Chuan Ni Victor L. Knoop Julian F. P. Kooij Bart van Arem |
| author_facet | Ying-Chuan Ni Victor L. Knoop Julian F. P. Kooij Bart van Arem |
| author_sort | Ying-Chuan Ni |
| collection | DOAJ |
| description | A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice. |
| format | Article |
| id | doaj-art-81f4252d1a5b45c8a1aa7d9d8aaa5a89 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-81f4252d1a5b45c8a1aa7d9d8aaa5a892025-01-24T00:02:49ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01525126410.1109/OJITS.2024.339514910510416Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based ApproachYing-Chuan Ni0https://orcid.org/0000-0003-1856-5707Victor L. Knoop1https://orcid.org/0000-0001-7423-3841Julian F. P. Kooij2https://orcid.org/0000-0001-9919-0710Bart van Arem3https://orcid.org/0000-0001-8316-7794Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zürich, Zürich, SwitzerlandDepartment of Transport and Planning, Delft University of Technology, Delft, The NetherlandsDepartment of Cognitive Robotics, Intelligent Vehicles Group, Delft University of Technology, Delft, The NetherlandsDepartment of Transport and Planning, Delft University of Technology, Delft, The NetherlandsA substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.https://ieeexplore.ieee.org/document/10510416/Adaptive cruise controlcar-followingdeep reinforcement learningmeasurement noisemulti-anticipationstring stability |
| spellingShingle | Ying-Chuan Ni Victor L. Knoop Julian F. P. Kooij Bart van Arem Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach IEEE Open Journal of Intelligent Transportation Systems Adaptive cruise control car-following deep reinforcement learning measurement noise multi-anticipation string stability |
| title | Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach |
| title_full | Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach |
| title_fullStr | Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach |
| title_full_unstemmed | Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach |
| title_short | Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach |
| title_sort | adaptive cruise control utilizing noisy multi leader measurements a learning based approach |
| topic | Adaptive cruise control car-following deep reinforcement learning measurement noise multi-anticipation string stability |
| url | https://ieeexplore.ieee.org/document/10510416/ |
| work_keys_str_mv | AT yingchuanni adaptivecruisecontrolutilizingnoisymultileadermeasurementsalearningbasedapproach AT victorlknoop adaptivecruisecontrolutilizingnoisymultileadermeasurementsalearningbasedapproach AT julianfpkooij adaptivecruisecontrolutilizingnoisymultileadermeasurementsalearningbasedapproach AT bartvanarem adaptivecruisecontrolutilizingnoisymultileadermeasurementsalearningbasedapproach |