Fetching the paper…
Reading the bibliography…
In the field of autonomous driving, developing safe and trustworthy autonomous driving policies remains a significant challenge.
On value discrepancy of imitation learning
Xu, T., Li, Z., Yu, Y., 2019 · 1911
Earlier work this paper cites.
General lane-changing model mobil for car-following models
Kesting, A., Treiber, M., Helbing, D., 2007 · 1999
Earlier work this paper cites.
Congested traffic states in empirical observations and microscopic simulations
Treiber, M., Hennecke, A., Helbing, D., 2000 · 2000
Earlier work this paper cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Levine, S., Kumar, A., Tucker, G., Fu, J., 2020 · 2005
Earlier work this paper cites.
Human-in-the-loop imitation learning using remote teleoperation
Mandlekar, A., Xu, D., Mart í · 2012
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
Puterman, M.L., 2014 · 2014
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P., 2015 · 2015
Earlier work this paper cites.
Constrained policy optimization, in: International conference on machine learning, PMLR. pp. 22–31
Achiam, J., Held, D., Tamar, A., Abbeel, P., 2017 · 2017
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D., 2017 · 2017
Earlier work this paper cites.
Imitating driver behavior with generative adversarial networks, in: 2017 IEEE intelligent vehicles symposium (IV), IEEE. pp. 204–211
Kuefler, A., Morton, J., Wheeler, T., Kochenderfer, M., 2017 · 2017
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, in: International conference on machine learning, PMLR. pp. 1861–1870
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S., 2018 · 2018
Earlier work this paper cites.
Behavioral cloning for lateral motion control of autonomous vehicles using deep learning, in: 2018 IEEE International Conference on Electro/Information Technology (EIT), IEEE. pp. 0228–0233
Sharma, S., Tewolde, G., Kwon, J., 2018 · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R.S., Barto, A.G., 2018 · 2018
Earlier work this paper cites.
Hg-dagger: Interactive imitation learning with human experts, in: 2019 International Conference on Robotics and Automation (ICRA), IEEE. pp. 8077–8083
Kelly, M., Sidrane, C., Driggs-Campbell, K., Kochenderfer, M.J., 2019 · 2019
Earlier work this paper cites.
Conservative q-learning for offline reinforcement learning
Kumar, A., Zhou, A., Tucker, G., Levine, S., 2020 · 2020
Earlier work this paper cites.
Learning to drive by imitation: An overview of deep behavior cloning methods
Ly, A.O., Akhloufi, M., 2020 · 2020
Earlier work this paper cites.
Responsive safety in reinforcement learning by pid lagrangian methods, in: International Conference on Machine Learning, PMLR. pp. 9133–9143
Stooke, A., Achiam, J., Abbeel, P., 2020 · 2020
Cited alongside, same era.
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning, in: 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE. pp. 1746–1752
Ye, F., Cheng, X., Wang, P., Chan, C.Y., Zhang, J., 2020 · 2020
Cited alongside, same era.
Confidence-aware reinforcement learning for self-driving cars
Cao, Z., Xu, S., Peng, H., Yang, D., Zidek, R., 2021 · 2021
Cited alongside, same era.
Learning to walk in the real world with minimal human effort, in: Conference on Robot Learning, PMLR. pp. 1110–1120
Ha, S., Xu, P., Tan, Z., Levine, S., Tan, J., 2021 · 2021
Cited alongside, same era.
Trustworthy safety improvement for autonomous driving using reinforcement learning
Cao, Z., Xu, S., Jiao, X., Peng, H., Yang, D., 2022 · 2022
Reward (mis) design for autonomous driving
Knox, W.B., Allievi, A., Banzhaf, H., Schmitt, F., Stone, P., 2023 · 2023
Later among the works it cites.
Midjourney ai art generator
Midjourney, 2023 · 2023
Later among the works it cites.
Autonomous vehicles drove more than 9m miles in california in 2023
Report, T.R., 2024 · 2023
Later among the works it cites.
Toward human-in-the-loop ai: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Wu, J., Huang, Z., Hu, Z., Lv, C., 2023 · 2023
Later among the works it cites.
Guarded policy optimization with imperfect online demonstrations
Xue, Z., Peng, Z., Li, Q., Liu, Z., Zhou, B., 2023 · 2023
Later among the works it cites.
Towards robust decision-making for autonomous driving on highway
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Huang, Z., Wu, J., Lv, C., 2022 · 2022
Cited alongside, same era.
A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective
Ju, Z., Zhang, H., Li, X., Chen, X., Han, J., Yang, M., 2022 · 2022
Cited alongside, same era.
Introducing chatgpt
OpenAI, 2022 · 2022
Cited alongside, same era.
Safe driving via expert guided policy optimization, in: Conference on Robot Learning, PMLR. pp. 1554–1563
Peng, Z., Li, Q., Liu, C., Zhou, B., 2022 · 2022
Cited alongside, same era.
Highway decision-making and motion planning for autonomous driving via soft actor-critic
Tang, X., Huang, B., Liu, T., Lin, X., 2022 · 2022
Cited alongside, same era.
Prioritized experience-based reinforcement learning with human guidance for autonomous driving
Wu, J., Huang, Z., Huang, W., Lv, C., 2022 · 2022
Cited alongside, same era.
Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning
Cao, Z., Jiang, K., Zhou, W., Xu, S., Peng, H., Yang, D., 2023 · 2023
Cited alongside, same era.
Yang, K., Tang, X., Qiu, S., Jin, S., Wei, Z., Wang, H., 2023 · 2023
Later among the works it cites.
Accelerating reinforcement learning for autonomous driving using task-agnostic and ego-centric motion skills, in: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. pp. 11289–11296
Zhou, T., Wang, L., Chen, R., Wang, W., Liu, Y., 2023 · 2023
Later among the works it cites.
Longitudinal control of automated vehicles: A novel approach by integrating deep reinforcement learning with intelligent driver model
Bai, L., Zheng, F., Hou, K., Liu, X., Lu, L., Liu, C., 2024 · 2024
Closest in time.
Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Fu, Z., Zhao, T.Z., Finn, C., 2024 · 2024
Closest in time.
Safety-aware causal representation for trustworthy offline reinforcement learning in autonomous driving
Lin, H., Ding, W., Liu, Z., Niu, Y., Zhu, J., Niu, Y., Zhao, D., 2024 · 2024
Closest in time.
Integrating big data analytics in autonomous driving: An unsupervised hierarchical reinforcement learning approach
Mao, Z., Liu, Y., Qu, X., 2024 · 2024
Closest in time.
Learning from active human involvement through proxy value propagation
Peng, Z.M., Mo, W., Duan, C., Li, Q., Zhou, B., 2024 · 2024
Closest in time.
Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles
SAE International, 2021 · 2024
Closest in time.
Reinforcement learning from human feedback for lane changing of autonomous vehicles in mixed traffic
Wang, Y., Liu, L., Wang, M., Xiong, X., 2024 · 2024
Closest in time.
V2x-vlm: End-to-end v2x cooperative autonomous driving through large vision-language models
You, J., Shi, H., Jiang, Z., Huang, Z., Gan, R., Wu, K., Cheng, X., Li, X., Ran, B., 2024 · 2024
Closest in time.
Hgrl: Human-driving-data guided reinforcement learning for autonomous driving
Zhuang, H., Chu, H., Wang, Y., Gao, B., Chen, H., 2024 · 2024
Closest in time.