Explanations in Autonomous Driving: A Survey

Omeiza, Daniel; Webb, Helena; Jirotka, Marina; Kunze, Lars

doi:10.48550/arxiv.2103.05154

Cited by 4 publications

(5 citation statements)

References 114 publications

(165 reference statements)

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“…The domain dependent XAI methods, dedicated to specific domain such as natural science or finance, cover around 12% of the examined articles. Other research perspectives: the requirements analysis for XAI applications [49][50][51][52][53][54], the social responsibility [55][56][57][58], the processed data types [59][60][61], and human-computer interaction (HCI) [62][63][64][65][66] are represented by circa 6%, 6%, 5%, and 5% of the examined articles, respectively. As illustrated in Figure 4b, we also quantitatively analyzed the distribution of the XAI application fields.…”

Section: Methodsmentioning

confidence: 99%

“…In the field of robotics (circa 2%), the considered articles address the subject of explainable reinforcement learning [71,72] as well as categorization of explanatory capabilities and requirements [51]. Examples of other application fields include autonomous driving [50], communication systems and networking [49,73], education [74,75], and social sciences [11].…”

Section: Methodsmentioning

confidence: 99%

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XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

Clement¹,

Kemmerzell²,

Abdelaal

et al. 2023

MAKE

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Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) aims to make black-box AI models more transparent and comprehensible for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners and data scientists to start with the development of XAI software and to optimally select the most suitable XAI methods. To tackle this challenge, we introduce XAIR, a novel systematic metareview of the most promising XAI methods and tools. XAIR differentiates itself from existing reviews by aligning its results to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. Through this mapping, we aim to create a better understanding of the individual steps of developing XAI software and to foster the creation of real-world AI applications that incorporate explainability. Finally, we conclude with highlighting new directions for future research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

Clement¹,

Kemmerzell²,

Abdelaal

et al. 2023

MAKE

View full text Add to dashboard Cite

show abstract

“…Intelligent and connected vehicle (ICV) is expected to revolutionise road transportation and is an inevitable trend in future [1,2]. Car-following is one of the basic traffic scenarios.…”

Section: Introductionmentioning

confidence: 99%

Car‐following strategy of intelligent connected vehicle using extended disturbance observer adjusted by reinforcement learning

Yan

Gao

et al. 2023

CAAI Trans on Intel Tech

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Disturbance observer‐based control method has achieved good results in the car‐following scenario of intelligent and connected vehicle (ICV). However, the gain of conventional extended disturbance observer (EDO)‐based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions, thus declining the car‐following performance. To solve this problem, a car‐following strategy of ICV using EDO adjusted by reinforcement learning is proposed. Different from the conventional method, the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy. Since the “equivalent disturbance” can be compensated by EDO to a great extent, the disturbance rejection ability of the car‐following method will be improved significantly. Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.

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“…However, segmentation just detects the static objects in the road environment, but they cannot explain more dynamic information like potential hazard awareness and complexity evaluation (Omeiza et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation

Zhao¹,

Yun²,

Chai³

et al. 2022

Preprint

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The complex driving environment brings great challenges to the visual perception of autonomous vehicles.It's essential to extract clear and explainable information from the complex road and traffic scenarios and offer clues to decision and control. However, the previous scene explanation had been implemented as a separate model. The black box model makes it difficult to interpret the driving environment. It cannot detect comprehensive textual information and requires a high computational load and time consumption.Thus, this study proposed a comprehensive and efficient textual explanation model. From 336k video frames of the driving environment, critical images of complex road and traffic scenarios were selected into a dataset.Through transfer learning, this study established an accurate and efficient segmentation model to obtain the critical traffic elements in the environment. Based on the XGBoost algorithm, a comprehensive model was developed. The model provided textual information about states of traffic elements, the motion of conflict objects, and scenario complexity. The approach was verified on the real-world road. It improved the perception accuracy of critical traffic elements to 78.8%. The time consumption reached 13 minutes for each epoch, which was 11.5 times more efficient than the pre-trained network. The textual information analyzed from the model was also accordant with reality. The findings offer clear and explainable information about the complex driving environment, which lays a foundation for subsequent decision and control. It can improve the visual perception ability and enrich the prior knowledge and judgments of complex traffic situations.

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Explanations in Autonomous Driving: A Survey

Cited by 4 publications

References 114 publications

XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

Car‐following strategy of intelligent connected vehicle using extended disturbance observer adjusted by reinforcement learning

Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation

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