Literatura científica selecionada sobre o tema "Safe urban driving"
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Artigos de revistas sobre o assunto "Safe urban driving"
Bhattacharya, Shelley, e Kristina Diaz. "Driving Habits of Older Adults". Kansas Journal of Medicine 5, n.º 4 (27 de novembro de 2012): 134–41. http://dx.doi.org/10.17161/kjm.v5i4.11423.
Texto completo da fonteRafi'ah, Rafi'ah, Iga Maliga, Asri Reni Handayani, Ana Lestari e Herni Hasifah. "Analysis of the Influence of Perception on Safety Riding Behavior in the Sumbawa Community". Jurnal Penelitian Pendidikan IPA 9, n.º 8 (25 de agosto de 2023): 6675–81. http://dx.doi.org/10.29303/jppipa.v9i8.4775.
Texto completo da fonteFarag, Wael. "Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks". Recent Patents on Computer Science 12, n.º 2 (25 de fevereiro de 2019): 120–27. http://dx.doi.org/10.2174/2213275911666181106160002.
Texto completo da fonteXu, Hui, e Jianping Wu. "What Road Elements are More Important than Others for Safe Driving on Urban Roads?" Promet - Traffic&Transportation 35, n.º 6 (20 de dezembro de 2023): 814–28. http://dx.doi.org/10.7307/ptt.v35i6.394.
Texto completo da fonteArshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam e Gon-Woo Kim. "Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment". Sensors 20, n.º 18 (5 de setembro de 2020): 5053. http://dx.doi.org/10.3390/s20185053.
Texto completo da fonteWang, Shaobo, Pan Zhao, Biao Yu, Weixin Huang e Huawei Liang. "Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments". Journal of Advanced Transportation 2020 (7 de novembro de 2020): 1–20. http://dx.doi.org/10.1155/2020/8894060.
Texto completo da fonteUrmson, Chris, Chris Baker, John Dolan, Paul Rybski, Bryan Salesky, William Whittaker, Dave Ferguson e Michael Darms. "Autonomous Driving in Traffic: Boss and the Urban Challenge". AI Magazine 30, n.º 2 (26 de fevereiro de 2009): 17. http://dx.doi.org/10.1609/aimag.v30i2.2238.
Texto completo da fonteInder, Silva e Shi. "Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments". Remote Sensing 11, n.º 23 (20 de novembro de 2019): 2723. http://dx.doi.org/10.3390/rs11232723.
Texto completo da fonteLiu, Yi, Ming Jian Yu e Ke Si You. "A Study on the Lane Width of Car-Only Urban Underground Road". Advanced Materials Research 838-841 (novembro de 2013): 1191–96. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1191.
Texto completo da fonteVadivelu, A., Mamidipaka Sai Roshini e Yamali Sravya. "Fine-Grained Multi-class Road Segmentation using MultiScale Probability Learning". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de maio de 2024): 1775–80. http://dx.doi.org/10.22214/ijraset.2024.61924.
Texto completo da fonteTeses / dissertações sobre o assunto "Safe urban driving"
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.
Texto completo da fonteThe 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
Livros sobre o assunto "Safe urban driving"
Thompson, William R., e Leila Zakhirova. Comparing the Four Main Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699680.003.0009.
Texto completo da fonteKajitvichyanukul, Puangrat, e Brian D'Arcy, eds. Land Use and Water Quality: The Impacts of Diffuse Pollution. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789061123.
Texto completo da fonteCapítulos de livros sobre o assunto "Safe urban driving"
Kallweit, Roland, Uwe Gropengießer, Jörn Männel e 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.
Texto completo da fonteChen, Yu, e 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.
Texto completo da fonteCao, Shicong, e 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.
Texto completo da fontePotter, Emily, e 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.
Texto completo da fonteBitterman, 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.
Texto completo da fontePereira Cavalheri, Emerson, e 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.
Texto completo da fonteBalkhi, Syed Arwa A., Bhesh Kumar Karki, Ligy Philip e 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.
Texto completo da fonteR 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.
Texto completo da fonteSchroeter, Ronald, Alessandro Soro e 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.
Texto completo da fonteJafari, Mostafa, e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Safe urban driving"
Krasowski, Hanna, Yinqiang Zhang e 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.
Texto completo da fonteAlbilani, Mohamad, e 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.
Texto completo da fonteDing, Yan, Xiaohan Zhang, Xingyue Zhan e 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.
Texto completo da fonteSun-Do Kim, Chi-Won Roh, Sung-Chul Kang e 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.
Texto completo da fonteLi, Penghao, Wen Hu, Yuanwang Deng e 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.
Texto completo da fonteNing, Chengwei, Hao Zhang, Haimin Weng e 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.
Texto completo da fonteGratzer, Alexander L., Maximilian M. Broger, Alexander Schirrer e 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.
Texto completo da fonteEmam, Mostafa, e 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.
Texto completo da fonteKaranam, Sai Krishna, Thibaud Duhautbout, Reine Talj, Veronique Cherfaoui, Francois Aioun e 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.
Texto completo da fonteThal, Silvia, Philip Wallis, Roman Henze, Ryo Hasegawa, Hiroki Nakamura, Sou Kitajima e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Safe urban driving"
Lambermont, Serge, e Niels De Boer. Unsettled Issues Concerning Automated Driving Services in the Smart City Infrastructure. SAE International, dezembro de 2021. http://dx.doi.org/10.4271/epr2021030.
Texto completo da fontePulugurtha, Srinivas S., e Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, janeiro de 2022. http://dx.doi.org/10.31979/mti.2022.1944.
Texto completo da fonteKwon, Jaymin, Yushin Ahn e Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, agosto de 2021. http://dx.doi.org/10.31979/mti.2021.2010.
Texto completo da fonteJameel, Yusuf, Paul West e Daniel Jasper. Reducing Black Carbon: A Triple Win for Climate, Health, and Well-Being. Project Drawdown, novembro de 2023. http://dx.doi.org/10.55789/y2c0k2p3.
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