Shape optimization of a torsion beam axle for improving vehicle handling performance

In this study, shape optimization was conducted for a vehicle’s rear suspension torsion beam to improve its dynamic handling performance. To determine the design variables affecting the vehicle roll characteristics, a sensitivity analysis was conducted using the result of a Taguchi experiment with 6 factors in 8 runs. The upper and lower-flange lengths and web thickness of the torsion beam section, as well as the vertical height difference between the inner and outer of torsion beams, were determined as design variables through sensitivity analysis of the opposite wheel travel test for optimization of the torsion beam axle. The Box–Behnken experimental design with 4 factors and 27 runs was performed using the selected design variables and by performing opposite wheel travel analysis according to the experimental design, and the response surface functions of the roll stiffness, roll steer coefficient, roll center height, and mass of the torsion beam were generated. Using these response functions, shape optimization was conducted for the torsion beam of the rear suspension system. Dynamic performance analysis was performed by applying the optimized H-shaped torsion beam to the rear suspension of the vehicle dynamics model, and it was validated that the dynamic response performance of the optimized vehicle was improved.     

Cyclist target and test setup for evaluation of cyclist-autonomous emergency braking

From 2018, Autonomous Emergency Braking (AEB) systems dedicated to avoid or mitigate passenger car-tocyclist collisions will be considered in the safety assessment by Euro NCAP. To test such systems, appropriate equipment has been developed in a project called CATS “Cyclist-AEB Testing System”, that has run between April 2014 and August 2016. Moreover, a proposal for the most relevant test scenarios was set up. The objective of the project was to provide proof to Euro NCAP of the relevance of the proposed test scenarios and of the feasibility of practical implementation of the scenarios and test setup. The process regarding the selection, verification and validation of test scenarios is described. The cooperation between 17 industrial partners (car manufacturers and automotive suppliers) in the CATS project has stimulated the harmonization and acceptance of the protocol, target and test setup. The process and intermediate results including the used methodology, have been reviewed by the German Federal Highway Research Institute (BASt) and have been shared on a regular basis during the project with stakeholders in Europe, Japan and the USA. Euro NCAP already indicated to consider the results of the CATS project as the main input to draft the test protocol, including scenarios and target for Cyclist-AEB systems in 2018 and 2020.     

MPC-BASED steering control for backward-driving vehicle using stereo vision

We propose a steering control algorithm for autonomous backward driving in a narrow corridor. Passable spaces are detected using a stereo camera, and the steering angle is controlled by a model predictive controller (MPC). For passable space detection, an UV-disparity map is calculated from the original disparity map. Information regarding passable spaces collected by the stereo camera is used in steering control. Backward driving requires the driver’s preemptive actions, which can be learned by experience because of the non-intuitive responses (the initial motion of the vehicle is opposite to the driver’s steering angle input). This occurs because a backward-driving vehicle is a non-minimum phase system. One of the most popular steering control algorithms is Stanley method, which is based on the feedback of lateral displacement error and heading angle error. The method is very intuitive and works well for forward driving, but it exhibits significant undershoot for backward driving cases. Furthermore, the method does not explicitly consider any constraints on control inputs and states. We designed a steering controller based on the MPC technique that requires future information but can handle constraints explicitly. Because we have near-future information from the stereo camera under limited passable spaces, MPC can be effectively implemented. We performed several simulations and experiments to show the performance and superiority of the suggested method over a simple feedback-based control algorithm.     

Direct tire force generation algorithm based on non-iterative nonlinear inverse tire model

The function of vehicle dynamics control system is adjusting the yaw moment, the longitudinal force and lateral force of a vehicle body through several chassis systems, such as brakes, steering and suspension. Individual systems such as ESC, AFS and 4WD can be used to achieve desired performance by controlling actuator variables. However, integrated chassis control systems that have multiple objectives may not simply achieve the desired performance by controlling the actuators directly. Usually those systems determine the required tire forces in an upper level controller and a lower level controller regulates the tire forces through the actuators. The tire force is controlled in a recursive way based on vehicle state measurement, which may not be sufficient for fast response. For immediate force tracking, we introduce a direct tire force generation method that uses a nonlinear inverse tire model, a pseudo-inverse model of vehicle dynamics and the relationship between longitudinal force and brake pressure.     

Multi-Component Model of Diesel Sprays Under High Injection Pressure

The combustion efficiency of a diesel engine depends not only on spray characteristics but also on fuel-air mixing characteristics. Based on the original spray model, a new spray model is established in this paper to accurately predict the diesel spray, and then a multi-component evaporation model is added into it. The model takes the influence of component concentration gradient and species on its evaporation rate in the liquid phase into account. This paper studies the spray characteristics (spray penetration, spray angle and spray morphology) and fuel-air mixing characteristics (spray area, spray volume and air entrainment mass) using the spray model, and the results are compared with the experimental results. The comparison shows that the simulated spray penetration and spray angle are close to the experimental results with the average deviations less than 3%. Moreover, this paper studies the spray area, spray volume and air entrainment using empirical formula under different conditions. And the maximum deviations of the spray volume, spray area and air entrainment mass are less than 5% as compared with the test values. Overall, this spray model can predict the diesel spray characteristics and fuel-air mixing characteristics under high injection pressure accurately.     

Feature Selection,Deep Neural Network and Trend Prediction

The literature generally agrees that longer-horizon (over a month) predictions make more sense than short-horizon ones. However, it’s an especially challenging task due to the lack of data (in unit of long horizon) and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis (SRA) algorithm to constrained SRA (cSRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network (DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.     

浙江省大跨桥梁科技成果回顾及创新发展研究

2000 年以后， 随着浙江省经济的快速增长， 依托省内跨越钱塘江、 甬江、 椒江、 瓯江等过江通道工程建设、 东部沿海跨各海湾通道 (含杭州湾) 工程建设、 舟山连岛工程建设的开展不断发展和创新， 大跨径桥梁的建设技术取得了巨大的成就， 通过对浙江省的大跨桥梁建设历程进行总结和对浙江省公路桥梁科技成果回顾分析， 提出未来浙江省大跨桥梁创新发展方向， 为浙江省公路桥梁未来十年发展提供参考。     

Moving between mobility cultures: what affects the travel behavior of new residents?

This paper analyzes the complex interdependencies between residential relocation and daily travel behavior by focusing on modal change. To help explain changes in daily travel patterns after a long distance move between cities the concept of urban mobility cultures is introduced. This comprehensive approach integrates objective and subjective elements of urban mobility, such as urban form and socio-economics on the one hand, and lifestyle orientations and mode preferences on the other, within one socio-technical framework. Empirically, the study is based on a survey conducted among people who recently moved between the German cities Bremen, Hamburg and the Ruhr area. Bivariate analyses and linear multiple regression models are applied to analyze changes in car, rail-based and bicycle travel. This is done by integrating variables that account for urban mobility cultures and controlling for urban form, residential preferences and socio-demographics. A central finding of this study is, that changes in the use of the car and rail-based travel are much more dependent on local scale, such as neighborhood type and residential preferences, whereas cycling is more affected by city-wide attributes, which we addressed as mobility culture elements.     

A multiscale superpixel-level salient object detection model using local-global contrast cue

The goal of salient object detection is to estimate the regions which are most likely to attract human’s visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.     

The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study

Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.