Through its new technology the ISCAD system provides up to 300 kW with a safe-to-touch 48 Volt battery.This results in a high-performance drive that is intrinsically electrically safe.The novel machine design requires...Through its new technology the ISCAD system provides up to 300 kW with a safe-to-touch 48 Volt battery.This results in a high-performance drive that is intrinsically electrically safe.The novel machine design requires new concepts for power electronics,control systems,and integration strategies.Further degrees of freedom yield to challenges and even more important to possibilities for all kinds of applications.The innovation of the design will be explained and adjustments for power electronics and control will be worked out.The focus of this paper is the 3rd generation of Intelligent Stator Cage Drive(ISCAD)prototypes.This first integrated and compact prototype has been used to re-equip a Geely Emgrand EV.The entire system and the mentioned process are described in detail.In addition,the in-house designed 48 V battery is highlighted.Simulations and measurements give an overall demonstration of the potential and the current status of the drive.展开更多
The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and beha...The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.展开更多
Background:(I)To describe the development and components of the automobile simulator driving behavior evaluation system developed by CRIR-Institut Nazareth et Louis-Braille;(II)to present the preliminary results of th...Background:(I)To describe the development and components of the automobile simulator driving behavior evaluation system developed by CRIR-Institut Nazareth et Louis-Braille;(II)to present the preliminary results of the content evaluation of the driving behavior evaluation grid.Methods:The evaluation system consists of five components:(I)the VS500M Car Simulator(Virage Simulation);(II)four VS500M driving scenarios,modified to minimize the occurrence of simulator sickness and expose subjects to commonly encountered driving situations on highways and city boulevards;(III)the Tobii Pro Glasses 2 eye tracking device;(IV)a car simulator driving behavior observation grid(DBOG);(V)a software application used during the behaviour evaluation phase,where synchronized video tracking,certain data from the simulator(e.g.,speed)and the DBOG grid are presented.Initially,the expected safe driving behaviors were identified,including 235 of a visual nature,supported by literature data and consultation of the project steering committee and an expert in driving assessment.Driving behaviors were assessed in 22 subjects without visual impairment(mean age 55±20 years).Subsequently,the items were revised to determine their relevance based on their importance in terms of road safety or on the frequency with which behaviors were observed among participants.For analysis purpose,the items of the DBOG were grouped according to their content,by type of expected driving behavior(e.g.,following a stop,look to the left and right before crossing the intersection)or element to be detected(e.g.,pedestrians).Results:Some visual behaviors are difficult to observe with the eye tracker device because they are more dependent on peripheral than central vision.Others are rarely observed,possibly because they are little or not realized in daily life or the representation of reality on the simulator does not stimulate their adoption.On the other hand,the visual detection behaviors expected in a situation where safety can be compromised are mostly carried out(e.g.,detection of oncoming vehicles,side mirror verification when changing lanes).Conclusions:This first phase of the study has made possible to better target the items to be kept in the car simulator driving behavior observation grid,and those that will have to be removed as they are too difficult to observe or too rarely adopted by the participants.Content validity and inter-rater reliability will be assessed from the simplified grid.展开更多
Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we ...Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.展开更多
无人驾驶技术的关键是决策层根据感知环节输入信息做出准确指令。强化学习和模仿学习比传统规则更适用于复杂场景。但以行为克隆为代表的模仿学习存在复合误差问题,使用优先经验回放算法对行为克隆进行改进,提升模型对演示数据集的拟合...无人驾驶技术的关键是决策层根据感知环节输入信息做出准确指令。强化学习和模仿学习比传统规则更适用于复杂场景。但以行为克隆为代表的模仿学习存在复合误差问题,使用优先经验回放算法对行为克隆进行改进,提升模型对演示数据集的拟合能力;原DDPG(deep deterministic policy gradient)算法存在探索效率低下问题,使用经验池分离以及随机网络蒸馏技术(random network distillation,RND)对DDPG算法进行改进,提升DDPG算法训练效率。使用改进后的算法进行联合训练,减少DDPG训练前期的无用探索。通过TORCS(the open racing car simulator)仿真平台验证,实验结果表明该方法在相同的训练次数内,能够探索出更稳定的道路保持、速度保持和避障能力。展开更多
文摘Through its new technology the ISCAD system provides up to 300 kW with a safe-to-touch 48 Volt battery.This results in a high-performance drive that is intrinsically electrically safe.The novel machine design requires new concepts for power electronics,control systems,and integration strategies.Further degrees of freedom yield to challenges and even more important to possibilities for all kinds of applications.The innovation of the design will be explained and adjustments for power electronics and control will be worked out.The focus of this paper is the 3rd generation of Intelligent Stator Cage Drive(ISCAD)prototypes.This first integrated and compact prototype has been used to re-equip a Geely Emgrand EV.The entire system and the mentioned process are described in detail.In addition,the in-house designed 48 V battery is highlighted.Simulations and measurements give an overall demonstration of the potential and the current status of the drive.
文摘The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.
文摘Background:(I)To describe the development and components of the automobile simulator driving behavior evaluation system developed by CRIR-Institut Nazareth et Louis-Braille;(II)to present the preliminary results of the content evaluation of the driving behavior evaluation grid.Methods:The evaluation system consists of five components:(I)the VS500M Car Simulator(Virage Simulation);(II)four VS500M driving scenarios,modified to minimize the occurrence of simulator sickness and expose subjects to commonly encountered driving situations on highways and city boulevards;(III)the Tobii Pro Glasses 2 eye tracking device;(IV)a car simulator driving behavior observation grid(DBOG);(V)a software application used during the behaviour evaluation phase,where synchronized video tracking,certain data from the simulator(e.g.,speed)and the DBOG grid are presented.Initially,the expected safe driving behaviors were identified,including 235 of a visual nature,supported by literature data and consultation of the project steering committee and an expert in driving assessment.Driving behaviors were assessed in 22 subjects without visual impairment(mean age 55±20 years).Subsequently,the items were revised to determine their relevance based on their importance in terms of road safety or on the frequency with which behaviors were observed among participants.For analysis purpose,the items of the DBOG were grouped according to their content,by type of expected driving behavior(e.g.,following a stop,look to the left and right before crossing the intersection)or element to be detected(e.g.,pedestrians).Results:Some visual behaviors are difficult to observe with the eye tracker device because they are more dependent on peripheral than central vision.Others are rarely observed,possibly because they are little or not realized in daily life or the representation of reality on the simulator does not stimulate their adoption.On the other hand,the visual detection behaviors expected in a situation where safety can be compromised are mostly carried out(e.g.,detection of oncoming vehicles,side mirror verification when changing lanes).Conclusions:This first phase of the study has made possible to better target the items to be kept in the car simulator driving behavior observation grid,and those that will have to be removed as they are too difficult to observe or too rarely adopted by the participants.Content validity and inter-rater reliability will be assessed from the simplified grid.
文摘Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.
文摘无人驾驶技术的关键是决策层根据感知环节输入信息做出准确指令。强化学习和模仿学习比传统规则更适用于复杂场景。但以行为克隆为代表的模仿学习存在复合误差问题,使用优先经验回放算法对行为克隆进行改进,提升模型对演示数据集的拟合能力;原DDPG(deep deterministic policy gradient)算法存在探索效率低下问题,使用经验池分离以及随机网络蒸馏技术(random network distillation,RND)对DDPG算法进行改进,提升DDPG算法训练效率。使用改进后的算法进行联合训练,减少DDPG训练前期的无用探索。通过TORCS(the open racing car simulator)仿真平台验证,实验结果表明该方法在相同的训练次数内,能够探索出更稳定的道路保持、速度保持和避障能力。
