随着城市的进步和不断发展,智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆,但在未来较长时间内人工驾驶车辆并不会被完全取代,此时出现网联车与人工驾驶车辆的混驾环境,即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆...随着城市的进步和不断发展,智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆,但在未来较长时间内人工驾驶车辆并不会被完全取代,此时出现网联车与人工驾驶车辆的混驾环境,即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆驾驶行为在路段内相互干扰,造成混合车流行驶效率低下。为减弱2种车辆间的相互作用,提出一种分离混驾环境下网联车和人工驾驶车辆的分阶段动态车道引导算法(dynamic lane guidance algorithm for separating CAVs and HDVs in mixed traffic environment,SCHME)。通过该算法分离在交叉口上游路段的混合流车辆集合,调整智能驾驶车辆的行驶路线并进行实时动态更新,在满足运动学约束收敛的条件下,人工驾驶车辆根据网联车的动态路线进行相应调整,实现在每辆车广义安全损失成本最小的情况下提高路段内混驾环境下车辆运行效率。通过MATLAB模拟车辆在进入交叉口前的车辆运行状态,结果表明,SCHME算法可在广义安全损失成本最小的情况下提高路段内平均车辆通行效率(17.29%),同时当车辆优化数组越大,车辆集合距离交叉口越远时,智能驾驶车辆渗透率越低,每辆车的道路广义安全损失成本越低。展开更多
Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving th...Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approach–The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system.If dynamic obstacles move randomly in the operation field,the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations.This paper considers the construction of the FIS,which consists of two controllers.The first controller uses three sensors based on the obstacles distances from the front,right and left.The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay.The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings–Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS.The reported simulation results are obtained using MATLAB software package.The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently,even in a complex environment that is populated with dynamic obstacles.The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications–The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions.The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles.However,the methodology is approached using two-dimensional analyses.Hence,the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value–This paper presents the design of a FIS and its characterizations in dynamic environments,specifically for obstacles that move at different velocities.This facilitates an improved functionality of the operation of UGV.展开更多
Artificial intelligence(AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, the...Artificial intelligence(AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty.Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted.This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability.In conclusion, this paper argues for the need for "fuzzy AI" in two senses:(i) the need for fuzzy methodologies(in the technical sense of Zadeh's fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and(ii) the need for fuzziness(in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.展开更多
文摘随着城市的进步和不断发展,智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆,但在未来较长时间内人工驾驶车辆并不会被完全取代,此时出现网联车与人工驾驶车辆的混驾环境,即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆驾驶行为在路段内相互干扰,造成混合车流行驶效率低下。为减弱2种车辆间的相互作用,提出一种分离混驾环境下网联车和人工驾驶车辆的分阶段动态车道引导算法(dynamic lane guidance algorithm for separating CAVs and HDVs in mixed traffic environment,SCHME)。通过该算法分离在交叉口上游路段的混合流车辆集合,调整智能驾驶车辆的行驶路线并进行实时动态更新,在满足运动学约束收敛的条件下,人工驾驶车辆根据网联车的动态路线进行相应调整,实现在每辆车广义安全损失成本最小的情况下提高路段内混驾环境下车辆运行效率。通过MATLAB模拟车辆在进入交叉口前的车辆运行状态,结果表明,SCHME算法可在广义安全损失成本最小的情况下提高路段内平均车辆通行效率(17.29%),同时当车辆优化数组越大,车辆集合距离交叉口越远时,智能驾驶车辆渗透率越低,每辆车的道路广义安全损失成本越低。
文摘Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approach–The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system.If dynamic obstacles move randomly in the operation field,the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations.This paper considers the construction of the FIS,which consists of two controllers.The first controller uses three sensors based on the obstacles distances from the front,right and left.The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay.The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings–Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS.The reported simulation results are obtained using MATLAB software package.The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently,even in a complex environment that is populated with dynamic obstacles.The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications–The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions.The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles.However,the methodology is approached using two-dimensional analyses.Hence,the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value–This paper presents the design of a FIS and its characterizations in dynamic environments,specifically for obstacles that move at different velocities.This facilitates an improved functionality of the operation of UGV.
文摘Artificial intelligence(AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty.Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted.This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability.In conclusion, this paper argues for the need for "fuzzy AI" in two senses:(i) the need for fuzzy methodologies(in the technical sense of Zadeh's fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and(ii) the need for fuzziness(in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.
