There have been significant advances in technology and automated systems that will eventually see the use of autonomous cars as commonplace on our roads. Various systems are already available that provide the driver with different levels of decision support. This paper highlights the key human factors issues associated with the interaction between the user and an autonomous system, including assistive decision support and the delegation of authority to the automobile. The level of support offered to the driver can range from traditional automated assistance, to system generated guidance that offers advice for the driver to act upon, and even more direct action that is initiated by the system itself without driver intervention. In many of these instances the role of the driver is slowly moving towards a supervisory role within a complex system rather than one of direct control of the vehicle. Different paradigms of interaction are considered and focus is placed on the partnership that takes place between the driver and the vehicle. Drawing on the wealth of knowledge that exists within the aviation domain and research literature that examines technology partnerships within the cockpit, this paper considers important factors that will assist the automotive community to understand the underlying issues of the human and their interaction within complex systems.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.apergo.2015.10.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle.
Biol Direct
December 2024
Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Background: Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging.
Methods: We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML).
Multi-sensor fusion and segmentation for autonomous vehicle multi-object tracking using deep Q networks.
Sci Rep
December 2024
School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car's sensors' ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks.
View Article and Find Full Text PDFThe role of lignin in 17β-estradiol biodegradation: insights from cellular characteristics and lipidomics.
Microb Cell Fact
December 2024
College of Veterinary Medicine, Jilin Agricultural University, Changchun, 130118, China.
17β-estradiol (E2) is an endocrine disruptor, and even trace concentrations (ng/L) of environmental estrogen can interfere with the endocrine system of organisms. Lignin holds promise in enhancing the microbial degradation E2. However, the mechanisms by which lignin facilitates this process remain unclear, which is crucial for understanding complex environmental biodegradation in nature.
View Article and Find Full Text PDFKalman Filter-Based Fusion of LiDAR and Camera Data in Bird's Eye View for Multi-Object Tracking in Autonomous Vehicles.
Sensors (Basel)
December 2024
Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt.
Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. Recent multi-modal approaches have improved performance but rely heavily on complex, deep-learning-based fusion techniques.
View Article and Find Full Text PDFAnalysis of the Changes in Volatile Components During the Processing of Enshi Yulu Tea.
Foods
December 2024
Key Laboratory of Tea Resources Comprehensive Utilization (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Fruit and Tea Research Institute, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
Volatile constituents are critical to the flavor of tea, but the changes in Enshi Yulu tea during the processing have not been clearly understood. Using headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) techniques, we analyze the aroma components of Enshi Yulu tea and changes in them during the processing stages. In total, 242 volatile compounds were identified.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!