Academic literature on the topic 'Safe urban driving'
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Journal articles on the topic "Safe urban driving":
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Bhattacharya, Shelley, and Kristina Diaz. "Driving Habits of Older Adults." Kansas Journal of Medicine 5, no. 4 (November 27, 2012): 134–41. http://dx.doi.org/10.17161/kjm.v5i4.11423.
Abstract:
BACKGROUND: The older adult population is the fastest growing cohort in Kansas, resulting in a growing number of older drivers. With age, changes in the ability to drive can compromise safety. Although it is challenging for health care providers to identify unsafe older drivers, it would be helpful to know what common driving habits they share. This exploratory study evaluated differences in the self-reported driving behaviors of older drivers in urban and rural settings of Kansas. METHODS: A one-page, 19-item survey was administered to patients over age 65 in the waiting rooms of two physician medical offices in urban Kansas City and rural Junction City, Kansas. RESULTS: A total of 105 surveys were completed. Rural drivers reported they were involved in approximately 9% more accidents than the urban drivers (p = 0.166). Rural drivers were more likely to drive in poor weather conditions, such as snow, ice, fog, and rain (p = 0.032). Eyeglasses were worn by 10% of the rural cohort compared to 37.8% of the urban cohort (p = 0.0044). More urban drivers reported they did not want to make changes to their current driving habits (71% vs 40%; p = 0.004). Urban drivers drove a longer distance to reach their destinations. Drivers from both environments avoided unfamiliar roads and did not use cell phones or global positioning system (GPS) devices while driving. CONCLUSIONS: By understanding the habits of older drivers, healthcare providers can tailor safe driving messages to support safe driving and enhance patient safety. Physicians could benefit from knowing that older rural drivers wore their glasses less frequently, trended towards having more accidents, and were more prone to drive during inclement weather. Urban Kansas drivers drove further to get to their destinations than their rural Kansas counterparts. Understanding these driving habits and tailoring their prevention messages accordingly may help health care providers in Kansas improve older patient’s safe driving behaviors.
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Rafi'ah, Rafi'ah, Iga Maliga, Asri Reni Handayani, Ana Lestari, and Herni Hasifah. "Analysis of the Influence of Perception on Safety Riding Behavior in the Sumbawa Community." Jurnal Penelitian Pendidikan IPA 9, no. 8 (August 25, 2023): 6675–81. http://dx.doi.org/10.29303/jppipa.v9i8.4775.
Abstract:
Unsafe driving behavior can have several negative impacts on society. Motorcyclists who exhibit safe and law-abiding driving behaviors are essential in minimizing the risk of accidents on the road. Perception plays a crucial role in driving safety, as it allows individuals to interpret and comprehend information received through their senses. This ability is crucial in identifying potential hazards and making quick decisions to avoid accidents. This study aims to analyze the influence of perceptions on safe driving behavior. Additionally, it observes the driving behavior of motorcyclists between urban and rural areas in Sumbawa. The research adopts a quantitative approach with a comparative study design. The sample consists of 100 respondents selected through purposive sampling and divided into two regions. The statistical analysis using simple linear regression in SPSS version 16.0 shows a significance value of 0.04 with an 8.1% influence for perception on safe driving behavior. The T-test results for safety riding observations in urban and rural areas in Sumbawa indicate a T-test of 0.886. In conclusion, there is a significant but very weak influence between perception and safe driving behavior. The observation results show no difference in driving behavior between urban and rural areas in Sumbawa
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Farag, Wael. "Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks." Recent Patents on Computer Science 12, no. 2 (February 25, 2019): 120–27. http://dx.doi.org/10.2174/2213275911666181106160002.
Abstract:
Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.
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Xu, Hui, and Jianping Wu. "What Road Elements are More Important than Others for Safe Driving on Urban Roads?" Promet - Traffic&Transportation 35, no. 6 (December 20, 2023): 814–28. http://dx.doi.org/10.7307/ptt.v35i6.394.
Abstract:
Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.
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Arshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam, and Gon-Woo Kim. "Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment." Sensors 20, no. 18 (September 5, 2020): 5053. http://dx.doi.org/10.3390/s20185053.
Abstract:
In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.
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Wang, Shaobo, Pan Zhao, Biao Yu, Weixin Huang, and Huawei Liang. "Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments." Journal of Advanced Transportation 2020 (November 7, 2020): 1–20. http://dx.doi.org/10.1155/2020/8894060.
Abstract:
An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.
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Urmson, Chris, Chris Baker, John Dolan, Paul Rybski, Bryan Salesky, William Whittaker, Dave Ferguson, and Michael Darms. "Autonomous Driving in Traffic: Boss and the Urban Challenge." AI Magazine 30, no. 2 (February 26, 2009): 17. http://dx.doi.org/10.1609/aimag.v30i2.2238.
Abstract:
The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.
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Inder, Silva, and Shi. "Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments." Remote Sensing 11, no. 23 (November 20, 2019): 2723. http://dx.doi.org/10.3390/rs11232723.
Abstract:
The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.
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Liu, Yi, Ming Jian Yu, and Ke Si You. "A Study on the Lane Width of Car-Only Urban Underground Road." Advanced Materials Research 838-841 (November 2013): 1191–96. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1191.
Abstract:
Theoretical analysis combined with experimental study was conducted to determine the proper lane width for urban car-only underground road. Vehicle trajectory data were collected in the experiment using three different ways including naturalistic driving experiment, video image processing and driving simulation. Lateral offset of each vehicle moving on the lane were obtained to determine the lane width. The results shows that for the car-only underground road, the design vehicle width is 1.8m,considering the safety margin and driving comfort, the minimum lane width 3m and 3.25m are reasonable and safe for the design speed 60km/h and 80km/h respectively.
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Vadivelu, A., Mamidipaka Sai Roshini, and Yamali Sravya. "Fine-Grained Multi-class Road Segmentation using MultiScale Probability Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1775–80. http://dx.doi.org/10.22214/ijraset.2024.61924.
Abstract:
Abstract: Driven road segmentation, a crucial part of advanced driver assistance systems, looks at the surroundings to keep vehicles within safe driving limits (ADASs). It begins by outlining the automatic lane detection shortfalls of conventional computer vision systems, pointing out problems such subpar segmentation, insufficient mask edge contours, sluggish processing, and restricted flexibility in intricate urban environments. Next, a multi-step procedure using deep learning networks is offered as a solution. This involves extracting vector skeletons, computing neighbouring pixels, assigning proportional weights depending on endpoints, and getting binary prediction masks. The conversation also touches on how self-driving technology will affect society, emphasizing how it could provide safe and intelligent transportation choices in the face of an increase in traffic accidents caused by careless drivers. Self-driving cars could be the first practical example of socially conscious robots interacting with humans, the story implies, even if it acknowledges possible public opposition. Furthermore, the story highlights the latest developments in autonomous driving technology, highlighting the vital requirement for strong sensing, perception, and cognitive technologies to enable completely autonomous vehicles that can adjust to changing road conditions.
Dissertations / Theses on the topic "Safe urban driving":
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Albilani, Mohamad. "Neuro-symbolic deep reinforcement learning for safe urban driving using low-cost sensors." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS008.
Abstract:
La recherche effectuée dans cette thèse concerne le domaine de la conduite urbaine sûre, en utilisant des méthodes de fusion de capteurs et d'apprentissage par renforcement pour la perception et le contrôle des véhicules autonomes (VA). L'évolution généralisée des technologies d'apprentissage automatique ont principalement propulsé la prolifération des véhicules autonomes ces dernières années. Cependant, des progrès substantiels sont nécessaires avant d'atteindre une adoption généralisée par le grand public. Pour accomplir son automatisation, les véhicules autonomes nécessitent l'intégration d'une série de capteurs coûteux (e.g. caméras, radars, LiDAR et capteurs à ultrasons). En plus de leur fardeau financier, ces capteurs présentent une sensibilité aux variations telles que la météo, une limitation non partagée par les conducteurs humains qui peuvent naviguer dans des conditions diverses en se fiant à une vision frontale simple. Par ailleurs, l'avènement des algorithmes neuronaux de prise de décision constitue l'intelligence fondamentale des véhicules autonomes. Les solutions d'apprentissage profond par renforcement, facilitant l'apprentissage de la politique du conducteur de bout en bout, ont trouvé application dans des scénarios de conduite élémentaires, englobant des tâches telles que le maintien dans la voie, le contrôle de la direction et la gestion de l'accélération. Cependant, il s'avère que ces algorithmes sont coûteux en temps d'exécution et nécessitent de large ensembles de données pour un entraînement efficace. De plus, la sécurité doit être prise en compte tout au long des phases de développement et de déploiement des véhicules autonomes.La première contribution de cette thèse améliore la localisation des véhicules en fusionnant les mesures des capteurs GPS et IMU avec une adaptation d'un filtre de Kalman, ES-EKF, et une réduction du bruit des mesures IMU. L'algorithme est déployé et testé en utilisant des données de vérité terrain sur un microcontrôleur. La deuxième contribution propose l'algorithme DPPO-IL (Dynamic Proximal Policy Optimization with Imitation Learning), conçu pour faciliter le stationnement automatisé en accordant une attention toute particulière à la sécurité. Cet algorithme apprend à exécuter des manœuvres de stationnement optimales tout en naviguant entre des d'obstacles statiques et dynamiques grâce à un entraînement complet intégrant des données simulées et réelles. La troisième contribution est un framework de conduite urbaine de bout en bout appelé guided hierarchical reinforcement Learning (GHRL). Il intègre des données de vision et de localisation ainsi que des démonstrations d'experts exprimées avec des règles ASP (Answer Set Programming) pour guider la politique d'exploration de l'apprentissage par renforcement hiérarchique et accélérer la convergence de l'algorithme. Lorsqu'une situation critique se produit, le système s'appuie également sur des règles liées à la sécurité pour faire des choix judicieux dans des conditions imprévisibles ou dangereuses. GHRL est évalué sur le jeu de données NoCrash du simulateur Carla et les résultats montrent qu'en incorporant des règles logiques, GHRL obtient de meilleures performances que les algorithmes de l'état de l'artThe research conducted in this thesis is centered on the domain of safe urban driving, employing sensor fusion and reinforcement learning methodologies for the perception and control of autonomous vehicles (AV). The evolution and widespread integration of machine learning technologies have primarily propelled the proliferation of autonomous vehicles in recent years. However, substantial progress is requisite before achieving widespread adoption by the general populace. To accomplish its automation, autonomous vehicles necessitate the integration of an array of costly sensors, including cameras, radars, LiDARs, and ultrasonic sensors. In addition to their financial burden, these sensors exhibit susceptibility to environmental variables such as weather, a limitation not shared by human drivers who can navigate diverse conditions with a reliance on simple frontal vision. Moreover, the advent of decision-making neural network algorithms constitutes the core intelligence of autonomous vehicles. Deep Reinforcement Learning solutions, facilitating end-to-end driver policy learning, have found application in elementary driving scenarios, encompassing tasks like lane-keeping, steering control, and acceleration management. However, these algorithms demand substantial time and extensive datasets for effective training. In addition, safety must be considered throughout the development and deployment phases of autonomous vehicles.The first contribution of this thesis improves vehicle localization by fusing data from GPS and IMU sensors with an adaptation of a Kalman filter, ES-EKF, and a reduction of noise in IMU measurements.This method excels in urban environments marked by signal obstructions and elevated noise levels, effectively mitigating the adverse impact of noise in IMU sensor measurements, thereby maintaining localization accuracy and robustness. The algorithm is deployed and tested employing ground truth data on an embedded microcontroller. The second contribution introduces the DPPO-IL (Dynamic Proximal Policy Optimization with Imitation Learning) algorithm, designed to facilitate end-to-end automated parking while maintaining a steadfast focus on safety. This algorithm acquires proficiency in executing optimal parking maneuvers while navigating static and dynamic obstacles through exhaustive training incorporating simulated and real-world data.The third contribution is an end-to-end urban driving framework called GHRL. It incorporates vision and localization data and expert demonstrations expressed in the Answer Set Programming (ASP) rules to guide the hierarchical reinforcement learning (HRL) exploration policy and speed up the learning algorithm's convergence. When a critical situation occurs, the system relies on safety rules, which empower it to make prudent choices amidst unpredictable or hazardous conditions. GHRL is evaluated on the Carla NoCrash benchmark, and the results show that by incorporating logical rules, GHRL achieved better performance over state-of-the-art algorithms
Books on the topic "Safe urban driving":
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Thompson, William R., and Leila Zakhirova. Comparing the Four Main Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699680.003.0009.
Abstract:
No two system leaders were identical in their claims to being the most innovative states in their respective zones, eras, and periods of leadership. Nonetheless, three general categories emerge: maritime commercial leadership, a pushing of agrarian boundaries, and sustained industrial economic growth. Those that made breakthroughs in the latter category, of course, redefined the modern world. Frontiers were critically important in all four cases of system leadership (China, the Netherlands, Britain, and the United States), but not exactly in the same way. Major improvements in transportation/communication facilitated economic growth by making interactions more feasible and less expensive, although the importance of trade varied considerably. Expanding populations were a hallmark of all four cases, even if the scale of increase varied. Population growth and urbanization forced agriculture to become more efficient and provided labor for nonagricultural pursuits. Urban demands stimulated regional specialization, technological innovation, and energy intensification, expanding the size of domestic markets and contributing to scalar increases in production. Just how large those scalar increases were depended on the interactions among technological innovation, power-driven machinery, and energy transition. Yet no single change led automatically to technological leadership. While lead status was never gained by default, it helped to have few rivals. As more serious rivals emerged, technological leaderships became harder to maintain.
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Kajitvichyanukul, Puangrat, and Brian D'Arcy, eds. Land Use and Water Quality: The Impacts of Diffuse Pollution. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789061123.
Abstract:
Abstract The influence of landscapes – topography, soil, vegetation, geology – on water quality is an inherent part of the global water cycle. Land use has adverse impacts for example when soils are exposed, significant quantities of pollutants are released (including anthropogenic materials added to those naturally present), or pollutants are added directly to the water environment. Those impacts range from industrial development to farming and urbanisation. Whilst inefficient polluting industrial effluents are still tolerated in some countries, and poorly treated sewage globally remains a huge challenge for sanitation and public health, as well as the water environment, diffuse pollution is relatively poorly recognised or understood. The operator of a sewage or trade effluent treatment plant is consciously discharging effluent to the local river. But a farmer is simply growing crops or farming livestock, a city commuter driving to work is unlikely to be thinking how brake pad wear has released copper to the water (and air) environment and hydrocarbons and particulates too; no one is intending to cause pollution of the water environment. The same applies to industrial chemists creating fire-proofing chemicals, solvents, fertilisers, pesticides, cosmetics and many more substances which contaminate the environment. Understanding and ultimately minimising diffuse pollution is in that sense the science of unintended consequences. And the consequences can be severe, for water resources and ecosystems. It's a global problem. This book comprises 18 papers from experts around the globe, presenting evidence from tropical as well as temperate regions, and rural as well as urban land use challenges. The book explores the nature of diffuse pollution and exemplifies the issues at various scales, from high-level national overviews to particular catchment and pollutant issues. By contrast, natural or semi-natural forest cover has long been recognised as safeguarding water quality in reservoirs (examples from Australia to Thailand and UK). The final chapter looks at how landscapes generally, can be designed to minimise pollution risks from particular land-uses, arguing for a more widespread catchment approach to water-aware landscape design, allied with flood risk resilience, place-making for people, and biodiversity opportunities too. ISBN: 9781789061116 (Paperback) ISBN: 9781789061123 (eBook) ISBN: 9781789061130 (ePub)
Book chapters on the topic "Safe urban driving":
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Kallweit, Roland, Uwe Gropengießer, Jörn Männel, and Rajanpreet Singh. "Safe and Robust Function Development for Urban Autonomous Driving Based on Agile Methodology and DevOps." In Proceedings, 1–9. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-34752-9_1.
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Chen, Yu, and Jie Chen. "Research on Residential Segregation in Chinese Cities." In The Urban Book Series, 57–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74544-8_5.
Abstract:
AbstractThis chapter provides an overview of research on the evolution of residential segregation in Chinese cities since the establishment of the People’s Republic of China. There were almost no discernible patterns of segregation during the central planning period, largely due to the socialist work-unit systems and the de-commodification of land and housing. Since the initiation of economic reforms in 1978, Chinese cities have witnessed significant spatial divisions across socioeconomic groups, driven by forces such as rapid economic and spatial restructuring, market-oriented housing and land reforms, and massive rural-to-urban migration. Residents of similar socio-economic status tend to cluster in the same neighbourhoods, with the elite moving to expensive gated communities and the urban poor to dilapidated residential areas. The impacts of segregation on residents’ social contactsand labour market outcomes are profound and long-lasting. While social segregation is regarded as a widespread urban phenomenon worldwide, the causes and consequences of segregation in Chinese cities should be interpreted within the country’s specific historical, social, cultural and institutional contexts.
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Cao, Shicong, and Hao Zheng. "A POI-Based Machine Learning Method for Predicting Residents’ Health Status." In Proceedings of the 2021 DigitalFUTURES, 139–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_13.
Abstract:
AbstractHealth environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field.
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Potter, Emily, and Katya Johanson. "From Streets to Silos: Urban Art Forms in Local Rural Government and the Challenge of Regional Development." In New Directions in Cultural Policy Research, 217–37. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32312-6_10.
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AbstractThis chapter discusses the Wimmera-Mallee Silo Art Trail in Victoria, Australia, as a case study that highlights the advantages and pitfalls of creative placemaking in a local government-driven rural development through cultural programmes. The Silo Art Trail showcases the benefits to communities of collaboration between a rural local council with higher levels of government and private corporations, when it is led by locally-generated needs and insights. At the same time, despite the achievements of the Silo Art Trail, a lack of explicit cultural policy and the different priorities of differing policy agencies created tensions between competing interests. Reflecting on this case offers opportunities to consider the ongoing significance of local government to cultural and economic development and strategies to strengthen its capacity to achieve positive impact, especially in rural contexts.
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Bitterman, Alex. "The Rainbow Connection: A Time-Series Study of Rainbow Flag Display Across Nine Toronto Neighborhoods." In The Life and Afterlife of Gay Neighborhoods, 117–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66073-4_5.
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AbstractRecently, the display and use of the rainbow flag in historically defined gay neighborhoods has grown even as gay residents and businesses have been driven away by gentrification, rising real-estate costs, and cultural homogenization. At the same time, prevelence and use of the rainbow flag and the rainbow motif has increased in areas not usually considered part of recognized gay neighborhoods. This chapter explores the prevalence and persistence of the display of the rainbow flag and rainbow motif in nine neighborhoods across Toronto, Ontario, Canada. The visual assessment of rainbow flag use across these neighborhoods serves as a potential model for examining the rate of spread of rainbow flags and visual rainbow motif symbols as a means for tracking the movement of the LGBTQ+ community across urban neighborhoods. Initial results suggest potential significance of the prevalence and persistence of the rainbow flag and the rainbow motif. These include; (1) a possible diaspora of LGBTQ+ residents from traditionally defined gay neighborhoods to newly emerging gay or LGBTQ-friendly neighborhoods, (2) a newfound inclusivity or pride among residents of other neighborhoods, and (3) “rainbow washing” due to overuse of the rainbow motif by non-LGBTQ businesses and organizations connected with pride celebrations. While overuse of the rainbow flag may diminish historically coded meaning of the rainbow, that well-intentioned use of the rainbow flag is a positive and welcoming indicator for LGBTQ+ individuals and it may lead to the emergence of additional LGBTQ-friendly enclaves that, over time, could potentially emerge as new gay neighborhoods.
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Pereira Cavalheri, Emerson, and Marcelo Carvalho dos Santos. "Road Maps and Sensor Integration for the Enhancement of Lane-Keeping Assistants." In Recent Topics in Highway Engineering - Up-to-date Overview of Practical Knowledge [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1005628.
Abstract:
Current efforts of vehicle manufacturers and research groups in designing and developing safer Intelligent Transportation Systems have revolved around achieving higher levels of driving automation for on-road vehicles. However, current approaches remain unable to assure safe vehicle autonomy in all conditions. Leveraging the communication between the infrastructure, for instance, the road geometry from high-definition maps, and vehicles could be a key enabler of safer Intelligent Transportation Systems. This combination would increase the overall traffic awareness which could benefit current automation approaches. In this study, a new lane-keeping system integrating information from a road map, satellite receiver, and inertial sensors is presented. Tests driving in complex urban environments showed that the proposed system kept the vehicle centered in the lanes during long satellite outages. This result was accomplished with a novel integration between the inertial and road map where the inertial was calibrated by the Map. The position cross-track accuracy upper and lower bounds, at 95% confidence, were 3 and 1 cm from achieving the control limit level (0.1 m) for Intelligent Transportation Location Based Systems. With these results, this work provides a new contribution to increase the robustness of current lane-keeping assistant approaches.
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Balkhi, Syed Arwa A., Bhesh Kumar Karki, Ligy Philip, and Shihabudheen M. Maliyekkal. "Water quality status and challenges in India and Nepal." In Technological Solutions for Water Sustainability: Challenges and Prospects, 13–23. IWA Publishing, 2023. http://dx.doi.org/10.2166/9781789063714_0013.
Abstract:
Water is a life-sustaining sector, and adequate access to safe water is fundamental to a healthy life and driving countries’ economic growth. It is an endowment from nature that must be protected and defended. However, the gross negligence and mismanagement of the water bodies, poor sanitation facilities, improper handling of liquid and solid wastes, and uncontrolled population growth pose a severe threat to the freshwater sector in developing economies like India. The widespread presence of healthcare products, pharmaceutically active compounds, and similar unregulated compounds in freshwater is an emerging concern due to their increasing societal dependence. These pollutants are causing additional threats to the dwindling freshwater reserves and posing significant challenges to achieving water quality objectives. The frequency of occurrence of geogenic pollutants, including radioactive pollutants, is also increased due to anthropogenic factors. Though various measures have been taken to address the problem, lack of planning, regulatory guidelines, poor institutional framework, and corruption have hampered the beneficial outcome. As a result, many underprivileged people from rural and urban areas in India and other nations in the Global South still lack access to safe drinking water. Considering the complexity of the problem, a comprehensive approach is needed to improve the existing condition and provide safe water to everyone. This chapter reviews various challenges pertaining to water quality in India and Nepal and solutions to obtain water quality objectives in a sustainable way.
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R Jeevitha, Dr. "AN OVERVIEW OF INTERNET OF VEHICLES (IOV)." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 6, 17–21. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bict6p1ch4.
Abstract:
The network which interconnects the cars, pedestrians and parts of urban infrastructure by using various sensors, built-in hardware and software to provide continuous communication is referred as the Internet of Vehicles (IoV). IoV is the major application of the Internet of Things (IoT) in the transportation field. The Internet of Vehicles is important for safer and efficient smart transportation. Internet of Vehicles (IoV) enables communication between vehicles and public networks using vehicle-to-vehicle (V2V) communication, V2R Vehicle-to-Road (V2R) communication, Vehicle-to-Human (V2H) communication and vehicle-to-sensor (V2S) interactions where the participants are intelligent objects. This chapter focuses on network architecture of IoV, features, benefits, applications, advantages of vehicle networking, new challenges in research and future of IoT in transportation. As IoV provides safe driving experience to the passengers, this chapter focuses on the importance of IoV, how vehicle connects to Internet and future scope of connected cars. Each entity in the network acts as an intelligent object. As IoV is evolved from the Vehicular Adhoc Network (VANET), there exist security and privacy issues which should be overcome by the connected intelligent objects.
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Schroeter, Ronald, Alessandro Soro, and Andry Rakotonirainy. "Social Cars." In Creating Personal, Social, and Urban Awareness through Pervasive Computing, 176–200. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4695-7.ch008.
Abstract:
Intelligent Transport Systems (ITS) encompass sensing technologies, wireless communication, and intelligent algorithms, and resemble the infrastructure for ubiquitous computing in the car. This chapter borrows from social media, locative media, mobile technologies, and urban informatics research to explore three classes of ITS applications in which human behavior plays a more pivotal role. Applications for enhancing self-awareness could positively influence driver behavior, both in real-time and over time. Additionally, tools capable of supporting our social awareness while driving could change our attitude towards others and make it easier and safer to share the road. Lastly, a better urban awareness in and outside the car improves our understanding of the road infrastructure as a whole. As a case study, the authors discuss emotion recognition (emotions such as aggressiveness and anger are a major contributing factor to car crashes) and a suitable basis and first step towards further exploring the three levels of awareness, self-, social-, and urban-awareness, in the context of driving on roads.
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Jafari, Mostafa, and Pete Smith. "Climate Change as a Driving Force on Urban Energy Consumption Patterns." In Advances in Public Policy and Administration, 547–63. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7661-7.ch043.
Abstract:
Heating degree days (HDD), in cases where temperatures are below 18°C, and cooling degree days (CDD), in cases where temperatures are above 21°C, were used as energy consumption indices. During the last half century, mean annual temperatures have increased, and as a consequence, CDD in the warm season have increased sharply. In the same time slice, HDD even in the cool and cold season have declined steadily. The number of monthly and annual total HDD (mean= 1556) are much higher than CDD (mean=400) in the case study area, and annual total HDD and CDD have a negative correlation (Pearson correlation = - 0.493; p = 0.001). The deceasing rate of HDD is limited and steady (R2= 0.062, p=0.099), but the increasing rate of CDD in the same time slice is sharp (R2=0.427, p=0.813). This shows that energy consumption patterns have increased sharply, and with available projection scenarios, it is projected to increase more rapidly, leading to higher energy costs.
Conference papers on the topic "Safe urban driving":
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Krasowski, Hanna, Yinqiang Zhang, and Matthias Althoff. "Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922166.
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Albilani, Mohamad, and Amel Bouzeghoub. "Guided Hierarchical Reinforcement Learning for Safe Urban Driving." In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00115.
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Ding, Yan, Xiaohan Zhang, Xingyue Zhan, and Shiqi Zhang. "Task-Motion Planning for Safe and Efficient Urban Driving." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341522.
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Sun-Do Kim, Chi-Won Roh, Sung-Chul Kang, and Jae-Bok Song. "A fuzzy decision making algorithm for safe driving in urban environment." In 2007 International Conference on Control, Automation and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iccas.2007.4406985.
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Li, Penghao, Wen Hu, Yuanwang Deng, and Pingyi Zhang. "Integrated Decision-Making and Planning Method for Autonomous Vehicles Based on an Improved Driving Risk Field." In SAE 2023 Intelligent Urban Air Mobility Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7112.
Abstract:
<div class="section abstract"><div class="htmlview paragraph">The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model. The results demonstrate that the proposed decision-making and planning method exhibits superior performance in addressing discrepancies related to vehicle acceleration and geometric. Besides, the improved driving risk field model is able to effectively model the various driving behavior in complex traffic scenarios, and has superior performance for reflecting the realistic driving risk distribution.</div></div>
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Ning, Chengwei, Hao Zhang, Haimin Weng, and Ran Ma. "Safe Architecture Design of Flight Control System for eVTOL." In SAE 2023 Intelligent Urban Air Mobility Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7101.
Abstract:
<div class="section abstract"><div class="htmlview paragraph">Advanced flight control system, aviation battery and motor technologies are driving the rapid development of eVTOL to offer possibilities for Urban Air Mobility. The safety and airworthiness of eVTOL aircraft and systems are the critical issues to be considered in eVTOL design process. Regarding to the flight control system, its complexity of design and interfaces with other airborne systems require detailed safety assessment through the development process. Based on SAE ARP4754A, a forward architecture design process with comprehensive safety assessment is introduced to achieve complete safety and hazard analysis. The new features of flight control system for eVTOL are described to start function capture and architecture design. Model-based system engineering method is applied to establish the functional architecture in a traceable way. SFHA and STPA methods are applied in a complementary way to identify the potential safety risk caused by failure and unsafe control action. PSSA with FTA assists to allocate safety requirements and modify the architecture of flight control system. Through the practice of safety-oriented architecture design of flight control system for eVTOL, safety requirements are identified, and related modifications and design are implemented to optimize the system architecture design. Comparing to the safety assessment method with only ARP4761 methods, the combination of ARP4761 and STPA will extend the perspective to deal with potential unsafety issues. Hazards caused by random failure and incorrected control are all tackled. The work of this paper can serve as a useful reference for the system safety assessment and architecture design for eVTOL and airborne systems.</div></div>
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Gratzer, Alexander L., Maximilian M. Broger, Alexander Schirrer, and Stefan Jakubek. "Flatness-Based Mixed-Integer Obstacle Avoidance MPC for Collision-Safe Automated Urban Driving." In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2023. http://dx.doi.org/10.1109/codit58514.2023.10284415.
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Emam, Mostafa, and Matthias Gerdts. "Deterministic Operating Strategy for Multi-objective NMPC for Safe Autonomous Driving in Urban Traffic." In 8th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011115400003191.
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Karanam, Sai Krishna, Thibaud Duhautbout, Reine Talj, Veronique Cherfaoui, Francois Aioun, and Franck Guillemard. "Virtual Obstacle for a Safe and Comfortable Approach to Limited Visibility Situations in Urban Autonomous Driving." In 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022. http://dx.doi.org/10.1109/iv51971.2022.9827372.
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Thal, Silvia, Philip Wallis, Roman Henze, Ryo Hasegawa, Hiroki Nakamura, Sou Kitajima, and Genya Abe. "Towards Realistic, Safety-Critical and Complete Test Case Catalogs for Safe Automated Driving in Urban Scenarios." In 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2023. http://dx.doi.org/10.1109/iv55152.2023.10186595.
Reports on the topic "Safe urban driving":
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Lambermont, Serge, and Niels De Boer. Unsettled Issues Concerning Automated Driving Services in the Smart City Infrastructure. SAE International, December 2021. http://dx.doi.org/10.4271/epr2021030.
Abstract:
Information and communication technology is fundamentally changing the way we live and operate in cities, such as instant access to events, transportation, bookings, payments, and other services. At the same time, three “megatrends” in the automotive industry—self-driving, electrification, and advanced manufacturing technology—are enabling the design of innovative, application-specific vehicles that capitalize on city connectivity. Applications could countless; however, they also need to be safe and securely integrated into a city’s physical and digital infrastructure, and into the overall urban ecosystem. Unsettled Issues Concerning Automated Driving Services in the Smart City Infrastructure examines the current state of the industry, the developments in automated driving and robotics, and how these new urban, self-driving city applications are different. It also analyzes higher level challenges for urban applications. Ultimately, this report includes several options for sharing lessons learned among different cities and their stakeholders.
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Pulugurtha, Srinivas S., and Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.1944.
Abstract:
Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.
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Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.
Abstract:
To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.
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Jameel, Yusuf, Paul West, and Daniel Jasper. Reducing Black Carbon: A Triple Win for Climate, Health, and Well-Being. Project Drawdown, November 2023. http://dx.doi.org/10.55789/y2c0k2p3.
Abstract:
Black carbon – also referred to as soot – is a particulate matter that results from the incomplete combustion of fossil fuels and biomass. As a major air and climate pollutant, black carbon (BC) emissions have widespread adverse effects on human health and climate change. Globally, exposure to unhealthy levels of particulate matter, including BC, is estimated to cause between three and six million excess deaths every year. These health impacts – and the related economic losses – are felt disproportionately by those living in low- and middle-income countries. Furthermore, BC is a potent greenhouse gas with a short-term global warming potential well beyond carbon dioxide and methane. Worse still, it is often deposited on sea ice and glaciers, reducing reflectivity and accelerating melting, particularly in the Arctic and Himalayas. Therefore, reducing BC emissions results in a triple win, mitigating climate change, improving the lives of more than two billion people currently exposed to unclean air, and saving trillions of dollars in economic losses. Today, the majority of BC emissions stem from just a handful of sectors and countries. Over 70% of BC comes from the residential and transportation sectors, with the latter being the dominant source in high-income countries and the former driving emissions in low- and middle-income nations. On a country-level, China and India are the biggest emitters accounting for one-third of global BC emissions. When combined with Brazil, Indonesia, and Nigeria, these five countries alone emit 50% of all BC. While BC emissions trends over the past 20 years have been inconsistent globally, there has been a notable decline in Europe, North America, and China. Conversely, emissions have been rising in regions like Africa, South Asia, and Central Asia. The Intergovernmental Panel on Climate Change recommends deep reductions in BC emissions by 2030 to achieve the Paris Climate Agreement goal of limiting warming to below 1.5°C, yet very few countries have addressed BC in their climate plans. Fortunately, solutions that can rapidly reduce BC emissions by the end of this decade are readily available. By implementing the right policies, deploying targeted interventions in hotspots, and redirecting climate finance, policymakers and funders can mitigate the climate effects of BC while saving millions of lives and trillions of dollars. Below are key recommendations to achieve these aims based on the findings of this report: Urgently implement clean cooking solutions Providing clean cooking fuels and technologies in sub-Saharan Africa and South Asia, especially in the hotspots of the Indo-Gangetic Plains, Nigeria, and Uganda, can significantly reduce BC emissions. Countries with low penetration of clean cooking fuel must urgently develop policies that make clean cooking a priority for health and climate. Target transportation to reduce current – and prevent future – emissions Retrofitting older diesel engines with diesel particulate filters can remove up to 95% of BC. Countries around the world must implement policies to phase out polluting vehicles, set emission standards, and accelerate the uptake of EVs and hybrids, especially in urban regions where transportation demand is growing rapidly. A successful shift to EVs demands national investments complemented with international financing and private capital. Multilateral development banks need to play a pivotal role in this transition, with strategies like concessional finance to fast-track key projects and stimulate private sector investment. Reduce BC from the shipping industry BC emissions from the shipping industry must be urgently reduced to protect the Arctic ecosystem. Shifting shipping away from heavy fuel oil and equipping ships with diesel particulate filters is a cost-effective approach that would quickly and significantly reduce emissions. Regulate air quality Stringent emissions standards, clean air laws, baselines, and mandatory monitoring programs can effectively reduce BC emissions. Such policies have already resulted in large reductions in Europe, North America, and, more recently, China. However, several low- and middle-income countries have no legal protection for ambient air quality and lack legislatively-mandated standards. Implementing strong and legally binding policies can result in a large decrease in BC emissions, particularly across the transportation and industry sectors. Include BC in nationally determined contributions and the UNFCCC Only 12 countries have explicitly addressed BC in their nationally determined contributions (NDCs). This limited focus on BC is partly due to its omission from the United Nations Framework Convention on Climate Change’s (UNFCCC) list of climate pollutants, an oversight that should be reconsidered given that reducing BC would save countless lives and slow global warming. As nations review their NDCs by 2025, they must incorporate BC reduction efforts to meet climate and well-being targets. Improve BC measurements and estimates BC estimates are plagued by uncertainties. Therefore, there is an urgent need for more accurate inventories in order to develop better emission reduction plans. Stakeholders must collaborate to develop a consistent BC measurement protocol, prioritize the collection of high-quality data, and use state of the art models to enhance estimates and reduce uncertainties.