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Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.

There are two shortcomings in the application of traditional A* algorithm in the path planning of autonomous driving. One is that the vehicle environment description method suitable for the A* algorithm is not given; the other is that the vehicle contours and kinematic constraints are not considered. Therefore, according to the characteristics of unstructured environment, this paper presents an environment description method combining global navigation layer and local planning layer, and proposes a local motion planning algorithm based on the improved A* algorithm for autonomous driving vehicles in unstructured environment. In the improved algorithm, profile collision is avoided by setting redundant security space, and the cost of path curvature is considered in heuristic function design. Compared with the original algorithm, it can improve the smoothness of the path, so as to get a path more satisfied with vehicle motion constraints. Simulation results show that the improved algorithm can avoid vehicle contours collision and output a smoother path.

This paper investigates the effect of using augmented reality (AR) annotations and two different gaze visualizations, head pointer (HP) and eye gaze (EG), in an AR system for remote collaboration on physical tasks. First, we developed a spatial AR remote collaboration platform that supports sharing the remote expert’s HP or EG cues. Then the prototype system was evaluated with a user study comparing three conditions for sharing non-verbal cues: (1) a cursor pointer (CP), (2) HP and (3) EG with respect to task performance, workload assessment and user experience. We found that there was a clear difference between these three conditions in the performance time but no significant difference between the HP and EG conditions. When considering the perceived collaboration quality, the HP/EG interface was statistically significantly higher than the CP interface, but there was no significant difference for workload assessment between these three conditions. We used low-cost head tracking for the HP cue and found that this served as an effective referential pointer. This implies that in some circumstances, HP could be a good proxy for EG in remote collaboration. Head pointing is more accessible and cheaper to use than more expensive eye-tracking hardware and paves the way for multi-modal interaction based on HP and gesture in AR remote collaboration.

In this study, an efficient topology optimization method under crash loads is proposed by combining the equivalent static loads with a model order reduction method, which is referred as the reduced model–based equivalent static loads method for nonlinear dynamic response topology optimization method. Considering that some parts of the vehicle experience large nonlinear deformations, whereas others exhibit only small linear deformations in a vehicle crash scenario, the linear and nonlinear behavior parts are identified and the whole model of the complete structure is divided into nonlinear and linear sub-models. At each cycle, the model order reduction method is used in the linear sub-model during crash analysis to solve the low-density-elements-induced mesh distortion problem and accelerate this process. In the linear static topology optimization, the nonlinear sub-model that was initially used to describe the nonlinear behavior part is linearized by the equivalent static loads method and then reduced by the Guyan reduction method. Then, the reduced equivalent static load model is assembled into the linear sub-model that is defined as the design space to formulate a reduced topology optimization model of the complete structure and the reduced equivalent static loads that only act on master degrees of freedom are calculated. Finally, the linear static topology optimization is performed based on the reduced topology optimization model with the reduced equivalent static loads to enhance the efficiency and improve the numerical stability. The process is repeated until the convergence criterion is satisfied. The effectiveness of the proposed method is demonstrated by investing a numerical example. The results show that the proposed method provides a feasible strategy for the topology optimization under crash loads, which can effectively improve the numerical stability and convergence.

This paper presents an efficient structural topology optimization strategy for crashworthiness. Although equivalent static loads (ESLs) provides a relatively effective way to solve nonlinear dynamic response topology optimization problems, efficiency is still required to improve. To overcome above disadvantage, the proposed method combines the ESLs with model reduction (MR) method. The ESLs method transforms crashworthiness topology optimization problems into a series of linear static topology problems. The model reduction scheme is applicable to reduce the linear matrix equation and ESLs to interface degrees of freedom (DOFs), by selecting interface DOFs between design and non-design space as master DOFs. Therefore, the linear static topology optimization is performed in a reduction manner and the efficiency is improved. The topology optimization results are filtered into a black-white design for the crash simulation to avoid the element distortion problem. The process is repeated until the convergence criteria is satisfied. A numerical example is used to verify the reliability of the proposed method. The results are compared with the dynamic response optimization method using ESLs (ESLSO). The results show that, the proposed method can efficiently solve structural topology optimization problems for crashworthiness.

In this paper, we describe a new Mixed Reality (MR) remote collaborative platform making use of 3D CAD models for training in the manufacturing industry. It enables a remote expert in Virtual Reality (VR) to train a local worker in a physical assembly task. For the local site, we use Spatial Augmented Reality (SAR) to enable the local worker see virtual cues without wearing any AR devices, leaving their user hands free to easily manipulate the physical parts. For the remote expert, we construct a 3D virtual environment using virtual replicas of the physical parts. We also report on the results of a usability study of the prototype. Refereed/Peer-reviewed

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

A hybrid energy storage system (HESS) composed of a battery and super-capacitor (SC) can make full use of advantages in energy and power density, which can further improve the performance of hybrid electric vehicles (HEV). Most studies have been limited to configuration and control to reduce the burden and prolong the life cycle of the battery, seldom focusing on the redesign of energy management strategies (EMS) for HEVs when passing from battery-only energy storage to HESS. In this paper, the equivalent fuel consumption during parallel-charging of a semi-active HESS in a series-parallel HEV under rule-based EMS was deduced, and a fuzzy-logic based (FLB) CS for the HESS was established using the state of energy (SOE) of the battery and SC as inputs. This allows the requirement of energy balance for HESS to be clearly expressed. Furthermore, the regulated EMS for HEV, which could dynamically manage the high efficiency of the engine and maintain the real-time charging/discharging capability of HESS, was proposed and verified under hardware in a loop test (HIL). Quantitative comparison of results between HESS and battery-only indicated that velocity-based SC'SOE-adjustable EMS presented in this paper could make better use of SC in filtering than SOE-constant EMS, and improve the fuel economy of HEVs from 22.76 l/100 km (battery-only) to 21.18 l/100 km. In order to emphasize the advantages of management, the electric energy usage/loss and efficiency under city driving-cycle were also presented.

Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rules are derived from analyzing the optimal trajectories, and it has the highest contribution to Hybrid Electric Vehicle (HEV). The methods of how to get the best performance is also educed. Using the new Rule-based power management strategy adopted from the optimal results, it is easy to demonstrate the effectiveness of the new strategy in further improvement of the fuel economy by the synergic hybrid system.

Under the current technical circumstances, the high cost of the electrical vehicle is the biggest problem of the mass marketization. So how to reduce the cost on the promise that the dynamic performance requirements be meet becomes more and more important. The purpose of this study is to develop a methodology to optimize an Extended-Range Electric Vehicle’s parameters taking minimum drivetrain cost and the best fuel economy as objectives. Design parameters, including electric motor peak power, engine rated power, and battery capacity are reasonably set. The simulation result illustrates that a set of vehicle dynamic performance constraints are met, in the same time the production cost is reduced and the fuel economy is improved. Further study should optimize the control strategy variables, too. Thus, the fuel economy can be optimized in a further extent. What’s new of this study in the field is taking production cost as one of the objectives to optimize the powertrain parameters. According to the ADVISOR simulation result, the fuel economy have been significantly improved and production cost has been reduced.