Behavior selector for autonomous vehicles using neural networks

Gonzalez-Miranda, Oscar; Zannatha, Juan Manuel Ibarra

doi:10.1109/comrob57154.2022.9962308

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…Weida Wang [24] Zhanqian Wu [26] Liang Tang [27] Tao Wang[29] Yingshi Guo [33] Shi [23], Lin Li [29] Xiao Wang [31] O.Olabiyi [32] Hao Zixu [34] Zhensong Wei [35] G. Miranda [25] Y. Jeong [30] Li Du [59],Simone Mentasti [65], Hengli Wang [67], Zhiyu Huang [68], Xianyong Yi [69],…”

Section: Simulation With Real Worldmentioning

confidence: 99%

A Comprehensive Review on Deep Learning-Based Motion Planning and End-to-End Learning for Self-Driving Vehicle

Ganesan,

Kandhasamy,

Chokkalingam

et al. 2024

IEEE Access

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Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber, and Tesla investing significantly in self-driving technology. These vehicles could transform commuting, offering safer, and efficient transport. A key SDV aspect is motion planning, generating secure, and efficient routes. This ensures safe navigation and prevents collisions with obstacles, pedestrians, and other vehicles. Deep Learning (DL) could aid SDV motion planning. AI tools and algorithms, like Artificial Neural Networks (ANNs), Machine Learning (ML) and DL can learn from data to create effective driving strategies, enhancing SDV adaptability to changing conditions for improved safety and efficiency. This survey gives a DL-based motion planning overview for SDVs, covering behaviour planning, trajectory planning, and End to End Learning (E2EL). It assesses various DL-based behaviour and trajectory planning methods, comparing and summarizing them. It also reviews diverse E2EL techniques including Imitation Learning (IL) and Reinforcement Learning (RL) gaining traction lately. Additionally, this review emphasizes the significance of two crucial enablers: datasets and simulation deployment frameworks for SDVs. The survey compares strategies using multiple metrics and highlights DL-based SDV implementation challenges, including simulation and real-world use cases. This article also suggests future research directions to address E2EL and DL-based motion planning limitations. The presented article is an excellent reference for scholars, engineers, and decision-makers who have an interest in DL-based SDV motion planning.

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