Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Article Computer Science, Artificial Intelligence

Perturbation-based methods for explaining deep neural networks: A survey

Maksims Ivanovs et al.

Summary: Deep neural networks have achieved state-of-the-art results in tasks dealing with perceptual data, but their black box nature hinders their full-scale application in safety-critical areas. In response to this, the field of explainable artificial intelligence has emerged. This survey focuses on perturbation-based XAI methods, exploring how these methods are applied to different data types.

PATTERN RECOGNITION LETTERS (2021)

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Review Chemistry, Analytical

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

De Jong Yeong et al.

Summary: Automated driving is becoming a pivotal technology in transforming the future of transportation with advancements in sensor and communication technology. Sensor calibration is crucial for ensuring the safety and feasibility of automated driving vehicles, while sensor fusion and obstacle detection techniques play a critical role in the successful implementation of autonomous driving systems.

SENSORS (2021)

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Article

Sight distances at unsignalized intersections: a comparison of guidelines and requirements for human drivers and autonomous vehicles

Zsófia Magyari et al.

Archives of Transport (2021)

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Review Computer Science, Artificial Intelligence

Explainable artificial intelligence: an analytical review

Plamen P. Angelov et al.

Summary: This paper provides a brief analytical review of the current state-of-the-art in explainability of artificial intelligence, discussing historical context, main challenges, recent methods, and future research directions.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2021)

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Proceedings Paper Computer Science, Hardware & Architecture

Evaluation of a Fail-Over Mechanism for 1oo2D Architectures in Highly-Automated Driving

Rupert Schorn et al.

Summary: Self-driving cars have made remarkable progress in autonomy capabilities, but still require human operators to monitor and take control in critical situations due to lack of sufficient dependability. Challenges towards achieving a truly dependable and trustworthy self-driving car include designing appropriate fault-tolerant architecture.

51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN-W 2021) (2021)

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Proceedings Paper Automation & Control Systems