Adaptivní asistent pro podélné parkování

• Thesis title in Czech: Adaptivní asistent pro podélné parkování
• Thesis title in English: Adaptive parallel parking assistant
• Key words: Automatické parkování|Zpětnovazebné učení|Neuronové sítě
• English key words: Automatic parking|Reinforcement learning|Neural networks
• Academic year of topic announcement: 2021/2022
• Thesis type: diploma thesis
• Thesis language: čeština
• Department: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
• Supervisor: Mgr. Roman Neruda, CSc.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 09.09.2022
• Date of assignment: 09.09.2022
• Confirmed by Study dept. on: 07.12.2023

Guidelines

The goal of the thesis is to develop a system for automatic parking motion planning of a vehicle described by a set of parameters. The system should utilize reinforcement learning optimization to develop an efficient parallel parking procedure for an ideal vehicle. To solve this task, a model-based deep reinforcement learning method that learns parking policy of the data from simulated environment and without human intervention or experience will be used. A transfer of the trained network-based policy to vehicles with specified dimensions and other parameters should be also considered. A development of suitable software tool for environment simulation and reinforcement learning will also be a part of the thesis outcome.

References

Balhara, Surjeet & Gupta, Nishu & Alkhayyat, Ahmed & Bharti, Isha & Malik, Rami & Mahmood, Sarmad & Abedi, Firas. (2022). A survey on deep reinforcement learning architectures, applications and emerging trends. IET Communications, 10.1049/cmu2.12447.

Ian Goodfellow and Yoshua Bengio and Aaron Courville. (2016) Deep Learning, MIT Press, http://www.deeplearningbook.org

Y Zhuang, Q Gu, B Wang, J Luo, H Zhang, W Liu (2018): Robust Auto-parking: Reinforcement Learning based Real-time Planning Approach with Domain Template, NeurIPS workshop on MLITS.

Zhang, Jiren & Chen, Hui & Song, Shaoyu & Hu, Fengwei. (2020). Reinforcement Learning-Based Motion Planning for Automatic Parking System. IEEE Access. pp. 1-1,. 10.1109/ACCESS.2020.3017770.