Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies
Matt Vitelli, Yan Chang, Yawei Ye, et al.
2022 International Conference on Robotics and Automation (ICRA) (2022), pp. 897-904
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

TransFuser: Imitation With Transformer-Based Sensor Fusion for Autonomous Driving
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022) Vol. 45, Iss. 11, pp. 12878-12895
Open Access | Times Cited: 148
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Davis Rempe, Jonah Philion, Leonidas Guibas, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 17284-17294
Open Access | Times Cited: 61
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
Wenzhao Zheng, Weiliang Chen, Yuanhui Huang, et al.
Lecture notes in computer science (2024), pp. 55-72
Closed Access | Times Cited: 9
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Yiren Lu, Justin Fu, George Tucker, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023), pp. 7553-7560
Open Access | Times Cited: 19
DriveIRL: Drive in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu, et al.
(2023), pp. 1544-1550
Closed Access | Times Cited: 17
Rethinking Imitation-based Planners for Autonomous Driving
Jie Cheng, Yingbing Chen, Xiaodong Mei, et al.
(2024), pp. 14123-14130
Closed Access | Times Cited: 5
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022), pp. 8652-8659
Open Access | Times Cited: 25
Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
Stefano Pini, Christian S. Perone, Aayush Ahuja, et al.
(2023), pp. 10069-10075
Open Access | Times Cited: 15
A Deep Reinforcement Learning Framework for Eco-Driving in Connected and Automated Hybrid Electric Vehicles
Zhaoxuan Zhu, Shobhit Gupta, Abhishek Gupta, et al.
IEEE Transactions on Vehicular Technology (2023) Vol. 73, Iss. 2, pp. 1713-1725
Open Access | Times Cited: 15
A survey of decision-making and planning methods for self-driving vehicles
Jun Hu, Yuefeng Wang, Shuai Cheng, et al.
Frontiers in Neurorobotics (2025) Vol. 19
Open Access
LiDAR-as-Camera for End-to-End Driving
Ardi Tampuu, Romet Aidla, Jan Aare van Gent, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2845-2845
Open Access | Times Cited: 12
DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving
Zhiyu Huang, Péter Karkus, Boris Ivanovic, et al.
(2024), pp. 6806-6812
Open Access | Times Cited: 3
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, et al.
(2024), pp. 629-636
Open Access | Times Cited: 3
InterSim: Interactive Traffic Simulation via Explicit Relation Modeling
Qiao Sun, Xin Huang, Brian Williams, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022), pp. 11416-11423
Open Access | Times Cited: 15
CCIL: Context-conditioned imitation learning for urban driving
Ke Guo, Wei Jing, Junbo Chen, et al.
(2023)
Open Access | Times Cited: 7
Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems
Luis Alberto Rosero, Iago Pachêco Gomes, Júnior Anderson Rodrigues Da Silva, et al.
(2024)
Open Access | Times Cited: 2
Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems
Luis Alberto Rosero, Iago Pachêco Gomes, Júnior Anderson Rodrigues da Silva, et al.
Sensors (2024) Vol. 24, Iss. 7, pp. 2097-2097
Open Access | Times Cited: 2
SDR – Self Driving Car Implemented using Reinforcement Learning & Behavioural Cloning
Sanjay S Tippannavar, S D Yashwanth, K M Puneeth
(2023), pp. 1-7
Closed Access | Times Cited: 5
Примитивы движения робота в задаче планирования траектории с кинематическими ограничениями
В. А. Головин, Konstantin Yakovlev
Informatics and Automation (2023) Vol. 22, Iss. 6, pp. 1354-1386
Open Access | Times Cited: 4
The Importance of Interpretability in AI Systems and Its Implications for Deep Learning
Muhammad Adnan
Advances in computational intelligence and robotics book series (2024), pp. 41-76
Closed Access | Times Cited: 1
Attentive hybrid reinforcement learning-based eco-driving strategy for connected vehicles with hybrid action spaces and surrounding vehicles attention
Menglin Li, Xiangqi Wan, Yan Mei, et al.
Energy Conversion and Management (2024) Vol. 321, pp. 119059-119059
Closed Access | Times Cited: 1
PEP: Policy-Embedded Trajectory Planning for Autonomous Driving
Dongkun Zhang, Jiaming Liang, Haojian Lu, et al.
IEEE Robotics and Automation Letters (2024) Vol. 9, Iss. 12, pp. 11361-11368
Closed Access | Times Cited: 1
FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy
Shuo Sun, Jie Chen, Jiawei Sun, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023), pp. 10527-10534
Closed Access | Times Cited: 4
Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
Sushant Veer, Karen Leung, Ryan K. Cosner, et al.
(2023), pp. 1507-1513
Open Access | Times Cited: 3
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
Bikun Wang, Zhipeng Wang, Chenhao Zhu, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023), pp. 1691-1696
Open Access | Times Cited: 3
Page 1 - Next Page

Scroll to top