Search Results (123)

With the increasing prevalence of autonomous vehicles (AVs), drivers’ spatial cognition and takeover performance have become critical to traffic safety. This study investigates the effects of landmark salience—specifically visual and structural salience—on drivers’ spatial cognition and takeover behavior in autonomous driving scenarios. Two simulator-based experiments were conducted. Experiment 1 examined the impact of landmark salience on spatial cognition tasks, including route re-cruise, scene recognition, and sequence recognition. Experiment 2 assessed the effects of landmark salience on takeover performance. Results indicated that salient landmarks generally enhance spatial cognition; the effects of visual and structural salience differ in scope and function in autonomous driving scenarios. Landmarks with high visual salience not only improved drivers’ accuracy in making intersection decisions but also significantly reduced the time it took to react to a takeover. In contrast, structurally salient landmarks had a more pronounced effect on memory-based tasks, such as scene recognition and sequence recognition, but showed a limited influence on dynamic decision-making tasks like takeover response. These findings underscore the differentiated roles of visual and structural landmark features, highlighting the critical importance of visually salient landmarks in supporting both navigation and timely takeover during autonomous driving. The results provide practical insights for urban road design, advocating for the strategic placement of visually prominent landmarks at key decision points. This approach has the potential to enhance both navigational efficiency and traffic safety. Full article

Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent malicious takeovers. This research proposes a novel approach to assessing the impact of cyber-attacks on autonomous vehicles and their surroundings, with a strong focus on prioritizing human safety. The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. It is validated using metrics such as MAE, loss function, and Spearman’s correlation through experiments on a dataset of 5410 samples. Beyond enhancing autonomous vehicle security, this research contributes to real-world attack impact assessment, ensuring human safety remains a priority in the evolving autonomous landscape. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article

Wastewater surveillance has proven to be a cost-effective, non-invasive method for monitoring the spread and evolution of SARS-CoV-2, yet its value during today’s low-incidence phase is still being defined. Between August 2023 and July 2024, 42 composite wastewater samples were collected in Perugia, Italy and analyzed using RT-qPCR and whole-genome sequencing to identify circulating SARS-CoV-2 lineages. In parallel, clinical samples (respiratory tract samples) were collected and analyzed, allowing for direct comparisons to confirm the robustness of the wastewater findings. The sewage viral loads ranged from 8.9 × 10⁵ to 4.9 × 10⁷ genome copies inhabitant⁻¹ day⁻¹, outlining two modest community waves (September–December 2023 and May–July 2024). Sequencing resolved 403 Omicron lineages and revealed three successive subvariant phases: (i) XBB.* dominance (August–October 2023), when late-Omicron XBB subvariants (mainly EG.5.* and XBB.1.5) accounted for almost all genomes; (ii) a BA.2.86/JN surge (November 2023–March 2024), during which the BA.2.86 subvariant, driven mainly by its JN descendants (especially JN.1), rapidly displaced XBB.* and peaked at 89% in February 2024; and (iii) KP.* takeover (April–July 2024), with JN.1-derived KP subvariants rising steadily and KP.3 reaching 81% by July 2024, thereby becoming the dominant lineage. Comparisons of data from wastewater and clinical surveillance demonstrated how the former presented a much higher diversity of circulating viral lineages. Importantly, some subvariants (including BA.2.86*) were detected in wastewater weeks to months prior to clinical identification, and for longer periods. Taken together, the obtained data validated wastewater surveillance as an effective early warning system, especially during periods of low infection prevalence and/or limited molecular testing efforts. This methodology can thus complement clinical surveillance by offering valuable insights into viral dynamics at the community level and enhancing pandemic preparedness. Full article

High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human–computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognition errors, number of operation errors, SUS scale, and NASA-TLX questionnaire for the icons were all analyzed using analysis of variance (ANOVA) and the nonparametric Mann–Whitney U test. The results show that anthropomorphic icons can reduce the drivers’ visual fatigue and mental load in frequent observation tasks due to the anthropomorphic facial features attracting driver attention through simple lines and improving visual search efficiency. However, for the sudden takeover human–computer collaboration task, the facial features of the anthropomorphic icons were not recognized in a short period of time. Additionally, due to the positive emotions produced by the facial features, the drivers did not perceive the suddenness and danger of the sudden takeover human–computer collaboration task, resulting in the traditional icons being more capable of arousing the drivers’ alertness and helping them take over the task quickly. At the same time, neither type of icon triggered misrecognition or operation for sufficiently skilled drivers. These research results can provide guidance for the design of icons in human–computer collaborative interfaces for different types of driving tasks in high-speed trains, which can help improve the recognition speed, reaction speed, and safety of drivers. Full article

Spoofing attacks pose a threat to drones, which can lead to their crash or takeover. As a countermeasure, the European Space Agency has implemented the Timed Efficient Loss-tolerant Authentication (TESLA) broadcast protocol in the Galileo Open Service Navigation Message Authentication (OSNMA) to detect such events. This study explores the application of TESLA in detecting spoofing attacks targeted at drone swarms that rely on positioning systems utilizing ultra-wideband (UWB) technology. The results of our experiments reaffirm that UWB-based positioning systems are not automatically invulnerable from spoofing attacks and that cryptographic methods such as TESLA are required to provide a layer of protection against spoofing attacks to detect them effectively. Full article

This article examines the audiovisual representation of violence during the armed takeover of the Ecuadorian television channel TC Television on 9 January 2024, an unprecedented event in the country’s recent media history. Employing a film analysis methodology, the study deconstructs the live broadcast by segmenting it into visual sequences and analyzing elements such as narrative content, shot composition, camera movement, sound design, and editing techniques. The interpretive phase includes narratological, iconic, and psychoanalytic readings. From a psychoanalytic perspective, the study explores the emotional impact of the broadcast on viewers, focusing on responses such as fear, anxiety, identification, projection, and the activation of psychological defense mechanisms. It also reflects on the broader sociocultural consequences of such representations of violence in public media. The article concludes by emphasizing the need for public investment in inclusive and high-quality education as a structural response to youth vulnerability, school dropout, and the risk of recruitment by organized criminal groups in Ecuador. Full article

In many applications, recognizing the depth of sleep (e.g., light, deep, REM sleep) while the subject is sleeping enables innovative features. For instance, in SAE Level 4 autonomous driving, a driver may need to takeover the vehicle control in case the autopilot is exiting its operational design domain. Depending on the depth of the sleep, the subject may need time to takeover effectively; hence, it is particularly relevant to know in which sleep stage the subject is (e.g., light sleep, deep sleep, and REM sleep), and possibly initiate actions to prevent the subject to remain in those sleep stages that lead to longer takeover time. Sleep stage classification can be achieved through an on-the-fly algorithm, which generates output in response to each input portion without knowledge of future inputs, unlike an off-Line algorithm that provides output just after receiving the entire input sequence. Various studies have analyzed algorithms or devices that identify sleep stages during the night; however, these typically require electroencephalography (EEG), which is obtrusive, or specialized devices. This study describes the development of an on-the-fly sleep-scoring algorithm using Heart Rate (HR), RR intervals, which is the distance between two consecutive heartbeats, and accelerometer data from a smartwatch, widespread, non-invasive, and affordable but accurate device. The subjects involved in our study wore a commercial off-the-shelf wearable device during a full night’s sleep, and were also monitored using a reference medical device to establish the ground truth by means of a full polysomnography (PSG) analysis. The on-the-fly sleep scoring algorithm based on smartwatch data was tested against PSG-based scoring, achieving 88.46% accuracy, 91.42% precision, and 93.52% sensitivity in sleep–wake identification. Deep sleep was correctly identified 69.38% of times, light sleep in 50.62%, REM sleep 62.02% and wakefulness 73.48% of times. Full article

Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction tendency) causes the overestimation of predicted loss caused by takeover. This paper proposes a novel HAIP safety assessment method considering effects of the above biases. First, an SA-based takeover decision model (SAB-TDM) is proposed. In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. Second, guided by SAB-TDM, a dynamic Bayesian network is used to assess HAIP safety. A case study on autonomous ship collision avoidance verifies effectiveness of the method. Results show that the above biases mutually contribute to seriously threaten HAIP safety. Full article

Ensuring the driver’s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. In this regard, this study proposes a hybrid LSTM-BiLSTM-ATTENTION algorithm for driver takeover performance prediction. By building a takeover scenario and conducting experiments in the driving simulation experimental platform under the human–machine co-driving environment, the relevant state indicators in the 15 s per second before the takeover request is sent are extracted from three perspectives, namely, driver state, traffic environment, and personal attributes, as model inputs, and the level of takeover performance was labeled; the hybrid LSTM-BiLSTM-ATTENTION algorithm is used to construct a driver takeover performance prediction model and compare it with other five algorithms. The results show that the algorithm proposed in this study performs optimally, with an accuracy of 93.11%, a precision of 93.02%, a recall of 93.28%, and an F1 score of 93.12%. This study provides new ideas and methods for realizing the accurate prediction of driver takeover performance, and it can provide a decision basis for the safe design of self-driving vehicles. Full article

Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the introduction of transparency design into urban rail transit driving tasks influences drivers’ situational awareness (SA), trust in automation (TiA), sense of agency (SoA), workload, operational performance, and visual behavior. Three transparency driver–machine interface (DMI) information conditions were evaluated: DMI1, which provided continuous feedback on vehicle operating status and actions; DMI1+2, which added inferential explanations; and DMI1+2+3, which further incorporated proactive predictions. Results from simulated driving experiments with 32 participants indicated that an appropriate level of transparency significantly enhanced TiA and SoA, thereby yielding the greatest acceptance. High transparency significantly aided in predictable takeover tasks but affected gains in TiA and SoA, increased workload, and disrupted perception-level SA. Compared with previous research findings, this study indicates the presence of a disparity in transparency needs for low-workload tasks. Therefore, caution should be exercised when introducing high-transparency designs in urban rail transit driving tasks. Nonetheless, an appropriate transparency interface design can enhance the driving experience. Full article

With the advancement of autonomous driving technology, especially the growing adoption of SAE Level 3 and above systems, drivers are transitioning from active controllers to supervisors who must take over in emergencies. For hearing-impaired drivers in a fatigued state, conventional voice alert systems often fail to provide timely and effective warnings, increasing safety risks. This study proposes an adaptive in-vehicle interface that combines visual and tactile feedback to address these challenges. Experiments were conducted to evaluate response accuracy, reaction time, and cognitive load under varying levels of driver fatigue. The findings show that the integration of visual and tactile cues significantly improves takeover efficiency and reduces mental strain in fatigued drivers. These results highlight the potential of multimodal designs in enhancing the safety and driving experience for hearing-impaired individuals. By providing practical strategies and evidence-based insights, this research contributes to the development of more inclusive and effective interaction designs for future autonomous driving systems. Full article

The advent of autonomous vehicles (AV) could revolutionize the automotive industry by significantly improving safety, efficiency, and accessibility. Despite their potential to improve traffic safety by reducing human error, their integration into existing transportation systems presents significant challenges. This is particularly evident in scenarios involving takeover events, where there is a transition of control from the vehicle to the human driver. Our driving simulator study, involving 14 drivers in a work-zone environment, provides critical insights into the takeover performance of level 3 to level 5 AVs. The findings suggest that the successful integration of AVs depends on their seamless incorporation into existing systems and the readiness of drivers to adapt to this emerging technology. Full article

Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3 AV takeovers are affected by human factors, traffic environment, and automatic driving systems. On this basis, this study proposes a conceptual framework of L3 AV takeovers. The main findings of this study include the following: (1) non-driving tasks, non-driving posture, individual characteristics, and trust have an impact on takeover behavior; (2) high traffic density, poor road geometry, and extreme weather have a negative impact on the takeover; (3) multimodal interaction design can improve collection performance. Although the existing research has made rich achievements, there are still many challenges. The influence of human factors on takeover performance is controversial, the quantification standard of takeover influencing factors is insufficient, and the prediction accuracy needs to be improved. It is suggested to refine the criteria of driver participation in NDRT, formulate an effective measurement standard of driver fatigue, and develop a takeover prediction model combining driver status and traffic environment conditions. It provides a research basis for the formulation of laws, infrastructure construction, and human–computer interaction design. Full article

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation. Full article