Auswahl der wissenschaftlichen Literatur zum Thema „Safe urban driving“
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Zeitschriftenartikel zum Thema "Safe urban driving"
Bhattacharya, Shelley, und Kristina Diaz. „Driving Habits of Older Adults“. Kansas Journal of Medicine 5, Nr. 4 (27.11.2012): 134–41. http://dx.doi.org/10.17161/kjm.v5i4.11423.
Der volle Inhalt der QuelleRafi'ah, Rafi'ah, Iga Maliga, Asri Reni Handayani, Ana Lestari und Herni Hasifah. „Analysis of the Influence of Perception on Safety Riding Behavior in the Sumbawa Community“. Jurnal Penelitian Pendidikan IPA 9, Nr. 8 (25.08.2023): 6675–81. http://dx.doi.org/10.29303/jppipa.v9i8.4775.
Der volle Inhalt der QuelleFarag, Wael. „Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks“. Recent Patents on Computer Science 12, Nr. 2 (25.02.2019): 120–27. http://dx.doi.org/10.2174/2213275911666181106160002.
Der volle Inhalt der QuelleXu, Hui, und Jianping Wu. „What Road Elements are More Important than Others for Safe Driving on Urban Roads?“ Promet - Traffic&Transportation 35, Nr. 6 (20.12.2023): 814–28. http://dx.doi.org/10.7307/ptt.v35i6.394.
Der volle Inhalt der QuelleArshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam und Gon-Woo Kim. „Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment“. Sensors 20, Nr. 18 (05.09.2020): 5053. http://dx.doi.org/10.3390/s20185053.
Der volle Inhalt der QuelleWang, Shaobo, Pan Zhao, Biao Yu, Weixin Huang und Huawei Liang. „Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments“. Journal of Advanced Transportation 2020 (07.11.2020): 1–20. http://dx.doi.org/10.1155/2020/8894060.
Der volle Inhalt der QuelleUrmson, Chris, Chris Baker, John Dolan, Paul Rybski, Bryan Salesky, William Whittaker, Dave Ferguson und Michael Darms. „Autonomous Driving in Traffic: Boss and the Urban Challenge“. AI Magazine 30, Nr. 2 (26.02.2009): 17. http://dx.doi.org/10.1609/aimag.v30i2.2238.
Der volle Inhalt der QuelleInder, Silva und Shi. „Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments“. Remote Sensing 11, Nr. 23 (20.11.2019): 2723. http://dx.doi.org/10.3390/rs11232723.
Der volle Inhalt der QuelleLiu, Yi, Ming Jian Yu und 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.
Der volle Inhalt der QuelleVadivelu, A., Mamidipaka Sai Roshini und Yamali Sravya. „Fine-Grained Multi-class Road Segmentation using MultiScale Probability Learning“. International Journal for Research in Applied Science and Engineering Technology 12, Nr. 5 (31.05.2024): 1775–80. http://dx.doi.org/10.22214/ijraset.2024.61924.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleThe 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
Bücher zum Thema "Safe urban driving"
Thompson, William R., und Leila Zakhirova. Comparing the Four Main Cases. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190699680.003.0009.
Der volle Inhalt der QuelleKajitvichyanukul, Puangrat, und Brian D'Arcy, Hrsg. Land Use and Water Quality: The Impacts of Diffuse Pollution. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789061123.
Der volle Inhalt der QuelleBuchteile zum Thema "Safe urban driving"
Kallweit, Roland, Uwe Gropengießer, Jörn Männel und 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.
Der volle Inhalt der QuelleChen, Yu, und 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.
Der volle Inhalt der QuelleCao, Shicong, und 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.
Der volle Inhalt der QuellePotter, Emily, und 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.
Der volle Inhalt der QuelleBitterman, 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.
Der volle Inhalt der QuellePereira Cavalheri, Emerson, und 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.
Der volle Inhalt der QuelleBalkhi, Syed Arwa A., Bhesh Kumar Karki, Ligy Philip und 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.
Der volle Inhalt der QuelleR 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.
Der volle Inhalt der QuelleSchroeter, Ronald, Alessandro Soro und 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.
Der volle Inhalt der QuelleJafari, Mostafa, und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Safe urban driving"
Krasowski, Hanna, Yinqiang Zhang und 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.
Der volle Inhalt der QuelleAlbilani, Mohamad, und 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.
Der volle Inhalt der QuelleDing, Yan, Xiaohan Zhang, Xingyue Zhan und 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.
Der volle Inhalt der QuelleSun-Do Kim, Chi-Won Roh, Sung-Chul Kang und 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.
Der volle Inhalt der QuelleLi, Penghao, Wen Hu, Yuanwang Deng und 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.
Der volle Inhalt der QuelleNing, Chengwei, Hao Zhang, Haimin Weng und 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.
Der volle Inhalt der QuelleGratzer, Alexander L., Maximilian M. Broger, Alexander Schirrer und 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.
Der volle Inhalt der QuelleEmam, Mostafa, und 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.
Der volle Inhalt der QuelleKaranam, Sai Krishna, Thibaud Duhautbout, Reine Talj, Veronique Cherfaoui, Francois Aioun und 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.
Der volle Inhalt der QuelleThal, Silvia, Philip Wallis, Roman Henze, Ryo Hasegawa, Hiroki Nakamura, Sou Kitajima und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Safe urban driving"
Lambermont, Serge, und Niels De Boer. Unsettled Issues Concerning Automated Driving Services in the Smart City Infrastructure. SAE International, Dezember 2021. http://dx.doi.org/10.4271/epr2021030.
Der volle Inhalt der QuellePulugurtha, Srinivas S., und Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, Januar 2022. http://dx.doi.org/10.31979/mti.2022.1944.
Der volle Inhalt der QuelleKwon, Jaymin, Yushin Ahn und 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.
Der volle Inhalt der QuelleJameel, Yusuf, Paul West und 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.
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