Night-time driving visibility associated with LED streetlight dimming

A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.

Improving the conspicuity and safety of pedestrians and cyclists on night-time roads

The visual limitations of drivers at night are a key contributing factor to the relatively high crash involvement of vulnerable road users including pedestrians, roadworkers and cyclists on night-time roads. Making vulnerable road users more conspicuous (recognisable, rather than simply visible) to oncoming drivers, is one approach to increasing their safety and has been a particular focus of my research. This review highlights the experimental approaches that our multidisciplinary research team have adopted to explore these issues, involving both closed and open road studies at night. One effective strategy to increase night-time conspicuity of vulnerable road users is clothing that includes retro-reflective materials on the moveable joints which, when illuminated in the headlight beam of oncoming vehicles, creates a strong sense of 'biological motion' or 'biomotion'. Our studies demonstrated that this basic visual perception allows drivers to accurately perceive the presence of a person, such as a pedestrian or cyclist, at much longer distances than when retro-reflective materials are positioned on the torso, as in high visibility vests. Subsequent studies demonstrated that the benefits of biomotion clothing are evident in cluttered environments, in the presence of glare, and for drivers of different ages and visual characteristics. Evidence gathered in these studies was instrumental in changing Australian and New Zealand standards governing high visibility clothing for roadworkers to include retro-reflective strips in the biomotion configuration. Ongoing studies are exploring how to make biomotion clothing attractive to vulnerable road users exercising at night, and how to ensure that the limitations of night-time vision and the importance of increasing night-time conspicuity are better understood. This body of research has involved collaborators from a range of disciplines who have been essential to understanding and addressing the visual challenges of night-time roads and assisted in translating this research into tangible benefits for night-time road safety.

Simulating the effect of different lighting conditions on left-turn driving behavior using a scenario-based anger method

Anger is a key factor affecting drivers' subjective judgment and driving skills. The influence of anger on driving behavior has been widely studied, but there is a lack of comparative research under different lighting conditions. Through a driving simulation experiment, this paper studies the influence of anger on left-turn driving behavior under two light conditions day and night. In the experiment, 32 licensed participants were divided into two groups, one in emotional neutrality and the other in anger. Among them, the emotional state of anger is induced by a traffic-related video. The results showed that compared with daytime participants, participants at night had higher anger intensity, shorter gap acceptance, and post encroachment time (PET) when left-turn driving. In addition, compared with neutral emotion participants, angry participants tended to accept shorter gap acceptance and PET when turning left. This indicates that participants failed to respond correctly to left-turn driving behavior in a state of anger. However, the response of gender differences to situational driving anger was not affected by light conditions. The anger intensity of male participants during the day and night was higher than that of female participants, and the gap between acceptance and PETs during left-turn was shorter than that of female participants. This shows that male participants are more likely to produce high-intensity anger and are more likely to make dangerous driving decisions in a state of anger. This paper puts forward some suggestions on identifying anger and preventing angry driving.

Yellow light decision based on driving style: Day or night?

Drivers’ driving decisions at yellow lights are an important cause of accidents at intersections. As proved by existing studies, driving style is an important basis for a driver to decide to pass a yellow light or not. This study, therefore, aims to investigate the effects of different driving styles on driving decisions at yellow lights under different lighting conditions. Specifically, 64 licensed drivers were recruited to comparative study the effects of different driving styles on the decision to pass through yellow lights under both daytime and nighttime lighting conditions using a driving simulator and a VR device. The results showed that maladjusted drivers more likely to pass the yellow light faster than adapted drivers (81.25% vs 43.75%) during both day and night. Male drivers had higher overall driving style scores than female drivers, and male drivers were faster and more likely to pass a yellow light than female drivers (56.25% vs 31.25%). This study also found that inexperienced drivers were faster and more likely to pass a yellow light than experienced drivers (50% vs 37.5%). Overall, maladjusted drivers are more likely to pass yellow lights, which can be improved and society properties by enhancing driving learning for maladjusted drivers.

Approaching autonomous driving with cautious optimism: analysis of road traffic injuries involving autonomous vehicles based on field test data

OBJECTIVES

To examine the patterns and associated factors of road traffic injuries (RTIs) involving autonomous vehicles (AVs) and to discuss the public health implications and challenges of autonomous driving.

METHODS

Data were extracted from the reports of traffic crashes involving AVs. All the reports were submitted to the California Department of Motor Vehicles by manufacturers with permission to operate AV test on public roads. Descriptive analysis and χ² analysis or Fisher's exact test was conducted to describe the injury patterns and to examine the influencing factors of injury outcomes, respectively. Binary logistic regression using the Wald test was employed to calculate the OR, adjusted OR (AOR) and 95% CIs. A two-tailed probability (p<0.05) was adopted to indicate statistical significance.

RESULTS

133 reports documented 24 individuals injured in 19 crashes involving AVs, with the overestimated incidence rate of 18.05 per 100 crashes. 70.83% of the injured were AV occupants, replacing vulnerable road users as the leading victims. Head and neck were the most commonly injured locations. Driving in poor lighting was at greater risk of RTIs (AOR 6.37, 95% CI 1.47 to 27.54). Collisions with vulnerable road users or incidents happening during commute periods led to a greater number of victims (p<0.05). Autonomous mode cannot perform better than conventional mode in road traffic safety to date (p=0.468).

CONCLUSIONS

Poor lighting improvement and the regulation of commute-period traffic and vulnerable road users should be strengthened for AV-related road safety. So far AVs have not demonstrated the potential to dramatically reduce RTIs. Cautious optimism about AVs is more advisable, and multifaceted efforts, including legislation, smarter roads, and knowledge dissemination campaigns, are fairly required to accelerate the development and acceptance.

Association between Crash Attributes and Drivers' Crash Involvement: A Study Based on Police-Reported Crash Data

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

Security Assessment of Urban Areas through a GIS-Based Analysis of Lighting Data Generated by IoT Sensors

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities’ lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.