Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.展开更多
Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a...Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.展开更多
This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the ...This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the Burg method, the fractional-order chaos system, and the Fuzzy Petri net (FPN) for early detection of AVS dysfunction. The Burg method is an autoregressive (AR) model that is used to estimate the frequency spectra of a phonoangiographic signal and to identify the spectral peaks in the region from 25 Hz to 800 Hz. In AVS, the frequency spectrum varies between normal blood flow and turbulent flow. The power spectra demonstrate changes in frequency and amplitude as the degree of stenosis changes. A screening system combining fractional-order chaos system and FPN is used to track the differences in the frequency spectra between the normal and stenosis access. The dynamic errors are indexes that can be used to evaluate the degree of AVS stenosis using a FPN. For 42 long-term follow-up patients, testing results show that the proposed screening system is more efficient in the evaluation of AVS stenosis.展开更多
文摘Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272332 and 51578199)Heilongjiang Provincial Natural Science Foundation(Grant No.YQ2021E031)the Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2022026).
文摘Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.
文摘This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the Burg method, the fractional-order chaos system, and the Fuzzy Petri net (FPN) for early detection of AVS dysfunction. The Burg method is an autoregressive (AR) model that is used to estimate the frequency spectra of a phonoangiographic signal and to identify the spectral peaks in the region from 25 Hz to 800 Hz. In AVS, the frequency spectrum varies between normal blood flow and turbulent flow. The power spectra demonstrate changes in frequency and amplitude as the degree of stenosis changes. A screening system combining fractional-order chaos system and FPN is used to track the differences in the frequency spectra between the normal and stenosis access. The dynamic errors are indexes that can be used to evaluate the degree of AVS stenosis using a FPN. For 42 long-term follow-up patients, testing results show that the proposed screening system is more efficient in the evaluation of AVS stenosis.
