Trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-68617-7_10
Reference10 articles.
1. van Hoek, R., Ploeg, J., Nijmeijer, H.: Cooperative driving of automated vehicles using b-splines for trajectory planning. IEEE Trans. Intell. Veh. 6(3), 594–604 (2021). https://doi.org/10.1109/TIV.2021.3072679
2. Li, H., Wu, C., Chu, D., Lu, L., Cheng, K.: Combined trajectory planning and tracking for autonomous vehicle considering driving styles. IEEE Access 9, 9453–9463 (2021). https://doi.org/10.1109/AC-CESS.2021.3050005
3. Arbabi, S., Tavernini, D., Fallah, S., Bowden, R.: Planning for au-tonomous driving via interaction-aware probabilistic action policies. IEEE Access 10, 81699–81712 (2022). https://doi.org/10.1109/AC-CESS.2022.3193492
4. Agostinho, L.R., Ricardo, N.M., Pereira, M.I., Hiolle, A., Pinto, A.M.: A practical survey on visual odometry for autonomous driving in challenging scenarios and conditions. IEEE Access 10, 72182–72205 (2022). https://doi.org/10.1109/ACCESS.2022.3188990
5. Li, B., Ouyang, Y., Li, L., Zhang, Y.: Autonomous driving on curvy roads without reliance on frenet frame: a cartesian-based trajectory planning method. IEEE Trans. Intell. Transp. Syst. 23(9), 15729–15741 (2022). https://doi.org/10.1109/TITS.2022.3145389
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