The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
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“However, several studies have been published on the performance of ADS-mounted sensors, but research analyzing the level of impact on the driving safety of AVs by ODD is still insufficient [16][17][18][19].”
From Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic (2024) · cites this paper as [17]
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B. A., Čáp, M., Yong, S. Z., Yershov, D. S., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,724
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00271 · Cited by 2,641
International Conference on Machine Learning (ICML)-2005
津田, 宏. (2006) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 2,558
Flagged first, then by citations
1 of the 65 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Bojarski, M., Yeres, P., Choromanska, A., Choromanski, K., Firner, B., Jackel, L., Muller, U., 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911. · arXiv
- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W., 2016. OpenAI Gym, arXiv preprint arXiv:1606.01540. · arXiv
- Bouton, M., Karlsson, J., Nakhaei, A., Fujimura, K., Kochenderfer, M.J., Tumova, J. 2019. Reinforcement learning with probabilistic guarantees for autonomous driving. arXiv preprint arXiv:1904.07189. · arXiv
- Hasselt, H., Guez, A., Silver, D., 2015. Deep reinforcement learning with double q-learning. arXiv:1509.06461. · arXiv
- Kahn, G., Villaflor, A., Pong, V., Abbeel, P., Levine, S. 2017. Uncertainty-aware reinforcement learning for collision avoidance. arXiv preprint arXiv:1702.01182. · arXiv
- Ma, X., Zhang, Q., Xia, L., Zhou, Z., Yang, J., Zhao, Q., 2020. Distributional Soft Actor Critic for Risk Sensitive Learning. arXiv:2004.14547. · arXiv
Cited by
Sorted by citations
Items 301–308 of 308
CoDPlacer: A Fast Approach for Digital IC Placement via Consistency Distillation
Shalmani, M. A., & Yuan, J. S. (2026) · Electronics · doi:10.3390/electronics15194529
Gu, C., Chen, G., Yu, Z., & Huang, F. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3994-8_20
Zhang, W., Song, Z., Zhang, P., & Chen, D. (2026) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-9853
Shielded reinforcement learning for industrial applications: A systematic literature survey
Odriozola-Olalde, H., Cano, F., Könighofer, B., Arana-Arexolaleiba, N., Zamalloa, M., & Perez-Cerrolaza, J. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200729
Ani, A., & Xing, F. (2026) · Urban Lifeline · doi:10.1007/s44285-026-00078-3
Learning safety-aware driving policies for unprotected left turns with barrier guidance
Ding, H., Bai, X., Fang, R., Bai, S., Cai, G., Zhuang, W., & Yin, G. (2026) · Mechatronics · doi:10.1016/j.mechatronics.2026.103618
Li, W., Ma, M., Liang, S., & Yu, K. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261487592
Gated Spline-Wavelet Kolmogorov-Arnold Recurrent Unit for multi-agent reinforcement learning
Lu, Y., Zhang, H., Wei, S., Huang, F., Liu, S., Wang, C., Huang, C., Peng, J., & Cheng, W. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.117094
Metadata
- Authors
- Li, G., Yang, Y., Li, S., Qu, X., Lyu, N., & Li, S. E.
- Affiliations
- Shenzhen University (CN)
- Tsinghua University (CN)
- Wuhan University of Technology (CN)
- Corresponding author
- Qu, Xingda
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 134
- Pages
- 103452
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W3211345831
- Cited by
- 308
- References
- 65
- Citations by year
- 2026: 39 · 2025: 86 · 2024: 85 · 2023: 64 · 2022: 32
- FWCI
- 14.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Funders
- National Natural Science Foundation of China (51805332)
- Shenzhen Fundamental Research and Discipline Layout project (JCYJ20190808142613246)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- autonomous vehicles, deep reinforcement learning, driving risk assessment, probabilistic risk model, position uncertainty, expected risk minimization, driving safety, CARLA simulator
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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