load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is base...load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is based on a single driving scene.However,the driver mental driving process.We proposed a driver mental load identification model which adapts to urban road traffie scenarios.scene discrimination sub-model can quickly and accurately determine the road traffic scene.The driver load identification sub-model Methods:The model includes a driving scene discrimination sub-model and driver load identification sub-model,in which the driving sub-model.selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification Results:The results show that the driving scene discrimination sub-model using five vehicle features as feature subsets has the best performance.The driver load identification sub-model based on the best feature subset reduces the feature noise,and the recognition tends to be consistent,and the support vector machine(5VM)algorithm is better than the K-nearest neighbors(KNN)algorithm.effect is better than the feature set using a single source signal and all data.The best recognition algorithm in different scenarios Conclusion:The proposed driver mental load identificution model can discriminate the driving scene quickly and accurately,and then identify the driver mental load.In this way,our model can be more suitable for actual driving and improve the effect of driver mental load identification.展开更多
Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-re...Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-regulation and driving cessation in order to suggest development of support systems to prolong older drivers’ safe mobility. Method: Three focus groups were conducted with 19 older active drivers aged 65+ who were divided according to annual mileage driven. Results: A content analysis revealed broad self-regulatory behaviour as already reported in the literature, e.g., avoiding driving at rush hour and at night. The participants also reported difficulty in finding the way to their final destination and an increasing need to plan their travelling. Co-piloting was a behaviour applied by couples to cope with difficulties encountered in traffic. A large part of the discussion was focused on emerging feelings of stress, anxiety and fear when driving in recent years, a feeling induced by external factors e.g., other road users’ behaviour, traffic density or high speed. Apart from health problems, high levels of stress could explain driving cessation, especially for women. An increased feeling of safety and comfort could be achieved by an increased use of support systems specifically designed to respond to older drivers’ needs. Conclusion: Support systems for older drivers should increase comfort and decrease their stress levels. New systems, such as co-pilot function and more developed Global Positioning System (GPS) supporting of the entire travel from door to door, should be developed to respond to the market needs.展开更多
This paper presents the technical survey and the trend analysis of the driver support technologies such as a pre-crush braking system in Japan. In the first part, Vehicle Intelligence to assist drivers is defined by t...This paper presents the technical survey and the trend analysis of the driver support technologies such as a pre-crush braking system in Japan. In the first part, Vehicle Intelligence to assist drivers is defined by two objective functions which are both TGA （Target Generation Agent） and TAA （Target Accomplishment Agent）. TAA is mainly based on the conventional technologies that are braking smoothly, or driving with lower fuel consumption. On the other hand, TGA has the intelligent function instead of human drivers. The actual TGA are explained using some concrete driver support systems. After that, Japanese market introduction date and evolution of driver support systems are discussed with clarifying cognitive aspects which are the perception support, the judgment support and the execution support. And Key technologies underlying evolution of driver support systems are explained. Finally the author concludes that the knowledge and insights needed for intelligent driver support systems will be much more complex than in the case of autonomous vehicles that drive themselves.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.52175088 and 52172399)the National Outstanding Youth Science Fund(NOYSF)in China(Grant No.52325211)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY19E050012).
文摘load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual Objective:At present,most research on driver mental load identification is based on a single driving scene.However,the driver mental driving process.We proposed a driver mental load identification model which adapts to urban road traffie scenarios.scene discrimination sub-model can quickly and accurately determine the road traffic scene.The driver load identification sub-model Methods:The model includes a driving scene discrimination sub-model and driver load identification sub-model,in which the driving sub-model.selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification Results:The results show that the driving scene discrimination sub-model using five vehicle features as feature subsets has the best performance.The driver load identification sub-model based on the best feature subset reduces the feature noise,and the recognition tends to be consistent,and the support vector machine(5VM)algorithm is better than the K-nearest neighbors(KNN)algorithm.effect is better than the feature set using a single source signal and all data.The best recognition algorithm in different scenarios Conclusion:The proposed driver mental load identificution model can discriminate the driving scene quickly and accurately,and then identify the driver mental load.In this way,our model can be more suitable for actual driving and improve the effect of driver mental load identification.
基金We acknowledge SAFER,Vehicle and Traffic Safety Centre at Chalmers,Gothenburg,Sweden,for funding this researchthe participants from the pensioner or-ganisations PRO and SPF in Jönköping,Sweden.
文摘Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-regulation and driving cessation in order to suggest development of support systems to prolong older drivers’ safe mobility. Method: Three focus groups were conducted with 19 older active drivers aged 65+ who were divided according to annual mileage driven. Results: A content analysis revealed broad self-regulatory behaviour as already reported in the literature, e.g., avoiding driving at rush hour and at night. The participants also reported difficulty in finding the way to their final destination and an increasing need to plan their travelling. Co-piloting was a behaviour applied by couples to cope with difficulties encountered in traffic. A large part of the discussion was focused on emerging feelings of stress, anxiety and fear when driving in recent years, a feeling induced by external factors e.g., other road users’ behaviour, traffic density or high speed. Apart from health problems, high levels of stress could explain driving cessation, especially for women. An increased feeling of safety and comfort could be achieved by an increased use of support systems specifically designed to respond to older drivers’ needs. Conclusion: Support systems for older drivers should increase comfort and decrease their stress levels. New systems, such as co-pilot function and more developed Global Positioning System (GPS) supporting of the entire travel from door to door, should be developed to respond to the market needs.
文摘This paper presents the technical survey and the trend analysis of the driver support technologies such as a pre-crush braking system in Japan. In the first part, Vehicle Intelligence to assist drivers is defined by two objective functions which are both TGA （Target Generation Agent） and TAA （Target Accomplishment Agent）. TAA is mainly based on the conventional technologies that are braking smoothly, or driving with lower fuel consumption. On the other hand, TGA has the intelligent function instead of human drivers. The actual TGA are explained using some concrete driver support systems. After that, Japanese market introduction date and evolution of driver support systems are discussed with clarifying cognitive aspects which are the perception support, the judgment support and the execution support. And Key technologies underlying evolution of driver support systems are explained. Finally the author concludes that the knowledge and insights needed for intelligent driver support systems will be much more complex than in the case of autonomous vehicles that drive themselves.
