End-to-End Self-Driving Approach Independent of Irrelevant Roadside Objects With Auto-Encoder

Wang, Tinghan; Luo, Yugong; Liu, Jinxin; Chen, Rui; Li, Keqiang

doi:10.1109/tits.2020.3018473

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Furthermore, Tinghan Wang et. al [70] introduced a novel approach for SDVs that is independent of irrelevant roadside objects, using an autoencoder architecture. The author offers a DL method that processes camera images in an E2E way, allowing the vehicle to make direction-finding decisions without the need for additional sensors or human intervention.…”

Section: Detailed Survey On End-to-end Learning For Sdvsmentioning

confidence: 99%

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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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Section: Detailed Survey On End-to-end Learning For Sdvsmentioning

confidence: 99%

“…Tinghan Wang [70], Satya R. Jaladi [73] Zhengyuan Yang [57], Tanmay Vilas Samak [64] ,Jie Hu [71], Lei Han [72], Nguyen Thi Hoai Thu [75], Oskar Natan [76] Junekyo Jhung [58], Tianhao Wu [60], José A. Diaz Amado [61],…”

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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“…DAEs have been used to improve upon model robustness on various tasks [29,38,1]. Wang et al [36] use an autoencoder to improve the accuracy of steering angle prediction by removing various roadside distractions such as trees or bushes. However, their focus is not robustness against perturbed images as only clean images are used in training.…”

Section: Related Workmentioning

confidence: 99%

AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning

Villarreal¹,

Poudel²,

Ryan³

et al. 2022

Preprint

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As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems have been deployed in various settings. For these systems to be trustworthy, we need to improve their robustness with adversarial training being one approach. In this work, we propose a gradientfree adversarial training technique, called AutoJoin. AutoJoin is a very simple yet effective and efficient approach to produce robust models for imaged-based autonomous maneuvering. Compared to other SOTA methods with testing on over 5M perturbed and clean images, AutoJoin achieves significant performance increases up to the 40% range under perturbed datasets while improving on clean performance for almost every dataset tested. In particular, AutoJoin can triple the clean performance improvement compared to the SOTA work by Shen et al. [31]. Regarding efficiency, AutoJoin demonstrates strong advantages over other SOTA techniques by saving up to 83% time per training epoch and 90% training data. The core idea of AutoJoin is to use a decoder attachment to the original regression model creating a denoising autoencoder within the architecture. This allows the tasks 'steering' and 'denoising sensor input' to be jointly learnt and enable the two tasks to reinforce each other's performance.

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End-to-End Learning with Memory Models for Complex Autonomous Driving Tasks in Indoor Environments

Lai

Bräunl

2023

J Intell Robot Syst

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The interest in autonomous vehicles has increased exponentially in recent years. While Lidar is a proven autonomous driving technology, end-to-end learning approaches have become popular as computer performance has improved. A fully end-to-end method—NVIDIA’s PilotNet has shown its ability to predict speed and steering angle with only camera images. This method achieved the Lidar-based methods’ performance in simple driving tasks. However, a significant drawback was no past spatiotemporal information, imposing an error-sensitive performance, especially in complex driving tasks. Spurred by this deficiency, this paper introduces two novel models: CNN + LSTM and CNN3D, aiming for complex autonomous driving tasks in indoor environments.

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End-to-End Self-Driving Approach Independent of Irrelevant Roadside Objects With Auto-Encoder

Cited by 9 publications

References 30 publications

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

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

AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning

End-to-End Learning with Memory Models for Complex Autonomous Driving Tasks in Indoor Environments

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