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131.
自动驾驶已经成为未来汽车技术发展的一个重要方向。但现阶段自动驾驶汽车的感知精度 不足已经成为限制自动驾驶汽车应用的一个重要因素。为解决上述问题,文章基于多传感器信息融合理论,提出一种自适应数据关联方法,分别考虑传感器的误差特性模型、目标的运动状态对数据关联的影响实现杂波环境中的目标追踪。并实验验证方法的有效性,实验结果表明,文章提出的融合感知结果能够有效地降低误差值,且目标轨迹追踪方法在所有实验场景中能 100%保证目标编号的一致性。 相似文献
132.
针对车辆转向下的操作稳定性和侧倾稳定性不足等问题,研究前轮主动转向的非线性滑膜控制,对汽车行驶稳定性研究有一定的指导意义。采用自适应高阶滑模控制方法结合 T-S模糊建模方法,考虑轮胎侧偏角非线性变化,构建模型参数观测器实时获取轮胎动态参数。通过 CarSim 和 MATLAB/Simulink 联合仿真,选取双移线和正弦停滞两种典型工况对质心侧偏角、横摆角速度、侧向加速度等性能指标的自适应高阶滑模控制方法进行仿真验证。结果表明,该方法能够改善线控转向系统对转角的动态响应性能;与传统滑模控制策略相比,该策略的横摆角速度和侧向加速度最大值分别降低了 22.63%和 5.4%,能够提高车辆的操纵稳定性。 相似文献
133.
文章针对辅助驾驶自适应巡航功能纵向控制车辆减速时车辆行驶顿挫的问题进行分析,通过上层控制器增加负扭矩请求判断逻辑来优化驾驶舒适性。车辆在跟车过程中纵向控制车辆减速时前后方向的直线运动平顺性得到提升,从而达到主观驾驶感受平稳舒适,为后续车型开发辅助驾驶自适应巡航功能调优提供有效的解决方案。随着车辆辅助驾驶功能以及性能的优化和完善,智能驾驶的舒适性感知问题将会得到改善,智能驾驶技术将得到飞速发展。 相似文献
134.
基于PSO的船舶动力定位自适应反步控制器 总被引：1，自引：1，他引：0
在船舶动力定位实际的作业过程中,由于海洋环境是缓慢变化的,建立的对象模型总存在一定的不确定性。针对上述问题,本文提出利用自适应反步控制器对船舶进行控制,并利用粒子群算法对控制器参数进行寻优,最后通过计算机仿真对本文方法进行了验证,仿真结果表明该方法有效。 相似文献
135.
We propose a novel real-time network-wide traffic signal control scheme which is (1) applicable under modern data technologies, (2) flexible in response to variations of traffic flows due to its non-cyclic feature, (3) operable on a network-wide and real-time basis, and (4) capable of considering expected route flows in the form of long-term green time ratios for intersection movement. The proposed system has a two-level hierarchical architecture: (1) strategy level and (2) control level. Considering the optimal states for a long-term period found in the strategy level, the optimal signal timings for a short-term period are calculated in the control level which consists of two steps: (1) queue weight update and (2) signal optimization. Based on the ratio of the cumulative green time to the desired green time is the first step to update the queue weights, which are then used in the optimization to find signal timings for minimum total delay. A parametric queue weight function is developed, discussed and evaluated. Two numerical experiments were given. The first demonstrated that the proposed system performs effectively, and the second shows its capability in a real-world network. 相似文献
136.
137.
J.M. Bergheau V. Robin F. Boitout . LTDS UMR CNRS/ECL/ENISE rue J. Parot Saint Etienne Cedex France .SYSTUS International Bvd Vivier Merle Lyon Cedex France 《上海交通大学学报(英文版)》2000,(1)
IntroductionAwiderangeofweldingorsurfacetreatmentprocessesinvolvetheuseofaheatsourcemovingataconstantspeedoverthesurfaceofthecomPonent.ThreedimensionalcomPutationofresidualstressesanddistortionsinducedbythiskindofprocesses,usingtheusualfiniteelementmethod,needsveryrefinedmeshesinordertocorrectlydescribethepropertiesandstressgradientsintheheataffCctCdzonealongthepathoftheheatsource.Inthispaper,wediscussthedifferentnumericalaPproaches,imPlementedinSYSWELD@software,whichcanbeusedtodorealisti… 相似文献
138.
The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time adaptation in response to the changing traffic network dynamics. Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers. One of the most efficient and robust type of RL algorithms are continuous state actor-critic algorithms that have the advantage of fast learning and the ability to generalize to new and unseen traffic conditions. These algorithms are utilized in this paper to design adaptive traffic signal controllers called actor-critic adaptive traffic signal controllers (A-CATs controllers).The contribution of the present work rests on the integration of three threads: (a) showing performance comparisons of both discrete and continuous A-CATs controllers in a traffic network with recurring congestion (24-h traffic demand) in the upper downtown core of Tehran city, (b) analyzing the effects of different traffic disruptions including opportunistic pedestrians crossing, parking lane, non-recurring congestion, and different levels of sensor noise on the performance of A-CATS controllers, and (c) comparing the performance of different function approximators (tile coding and radial basis function) on the learning of A-CATs controllers. To this end, first an agent-based traffic simulation of the study area is carried out. Then six different scenarios are conducted to find the best A-CATs controller that is robust enough against different traffic disruptions. We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently outperforms them. 相似文献
139.
In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this research, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. It is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network. 相似文献
140.
The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration. We formulate this problem as a 0–1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR. These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions. 相似文献