Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees

He, Xiangkun; Huang, Wenhui; Lv, Chen

doi:10.1016/j.eng.2023.10.005

Cited by 10 publications

(2 citation statements)

References 42 publications

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“…The TTA mainly determines whether the vehicle can pass through the area to eliminate the untraversable area. However, for traversable areas, vehicles may pass with some costs, such as safety costs [64,65], energy costs [51,66], and comfort costs [39,67]. Generally, the costs are estimated based on the previous TTA output and then the estimation results such as cost function [68] or cost map [69,70] can be obtained and passed to the path planner [34].…”

Section: Cost Estimationmentioning

confidence: 99%

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Wang,

Li,

Zhang

et al. 2024

Machines

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Autonomous driving in unstructured environments is crucial for various applications, including agriculture, military, and mining. However, research in unstructured environments significantly lags behind that in structured environments, mainly due to the challenges posed by harsh environmental conditions and the intricate interactions between vehicles and terrains. This article first categorizes unstructured path planning into hierarchical and end-to-end approaches and then the special parts compared to structured path planning are emphatically reviewed, such as terrain traversability analysis, cost estimation, and terrain-dependent constraints. This article offers a comprehensive review of the relevant factors, vehicle–terrain interactions, and methods of terrain traversability analysis. The estimation methods of safety cost, energy cost, and comfort cost are also emphatically summarized. Moreover, the constraints caused by the limits of terrains and vehicles are discussed. The applications of algorithms in recent articles for path planners are reviewed. Finally, crucial areas requiring further research are analyzed in unstructured path planning.

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Section: Cost Estimationmentioning

confidence: 99%

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Wang,

Li,

Zhang

et al. 2024

Machines

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“…A UTONOMOUS driving has been a large area of development, both commercially and in academic research, over the last decade or more [1], and continues to be an area of deep interest in the research community. However, while perception and control tasks are still undergoing significant research [2], [3], there has been an increase in interest in incorporating elements of trustworthiness [4], [5] and explainability [6]- [8] into autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Subjective Scoring Framework for VQA Models in Autonomous Driving

Rekanar,

Ahmed,

Mohandas

et al. 2024

IEEE Access

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The development of vision and language transformer models has paved the way for Visual Question Answering (VQA) models and related research. There are metrics to assess the general accuracy of VQA models but subjective assessment of the answers generated by the models is necessary to gain an in-depth understanding and a framework for subjective assessment is required. This work develops a novel scoring system based on the subjectivity of the question and analyses the answers provided by the model using multiple types of natural language processing models (bert-base-uncased, nli-distilBERT-base, allmpnet-base-v2 and GPT-2) and sentence similarity benchmark metrics (Cosine Similarity). A case study detailing the use of the proposed subjective scoring framework on three prominent VQA models-ViLT, ViLBERT, and LXMERT using an automotive dataset is also presented. The framework proposed aids in analyzing the shortcomings of the discussed VQA models from a driving perspective and the results achieved help determine which model would work best when fine-tuned on a driving-specific VQA dataset.

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Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

Zhang,

Li,

Chen

et al. 2025

Accident Analysis & Prevention

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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees

Cited by 10 publications

References 42 publications

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Subjective Scoring Framework for VQA Models in Autonomous Driving

Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

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