Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowl...Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.展开更多
This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical l...This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical limitations on charging and cooling power is considered along with more detailed health models.Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework.A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge,charging rate,temperature and time.The optimization results demonstrate an improvement over the benchmark constant current–constant voltage(CCCV)charging protocol when considering both the charging time and battery health.A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol.In a case study,for example,the‘optimal time’charging is found to take 12 min while the‘optimal health’charging profile suggests around 100 min for charging the battery from 25 to 75%state-of-charge.Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.展开更多
文摘Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
文摘This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical limitations on charging and cooling power is considered along with more detailed health models.Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework.A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge,charging rate,temperature and time.The optimization results demonstrate an improvement over the benchmark constant current–constant voltage(CCCV)charging protocol when considering both the charging time and battery health.A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol.In a case study,for example,the‘optimal time’charging is found to take 12 min while the‘optimal health’charging profile suggests around 100 min for charging the battery from 25 to 75%state-of-charge.Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.
