汽车智能网联系统中的信息安全问题探究

汽车智能网联系统是当前智能化汽车发展的核心方向,对于汽车性能提升以及汽车使用安全都有非常重要的作用,本文笔者主要针对汽车智能网联系统进行了分析研究,文章中简要阐述了汽车智能网联系统及技术,并提出了汽车智能网联系统中存在的信息安全问题,并针对问题提出汽车智能网联系统的信息安全建设策略。     

我国二手车市场的现状与前景分析

近年来,我国的二手车市场不断地发展,逐年呈递增趋势,尤其是在本世纪以来近二十年的时间中,二手车的交易量倍增至近60倍。本文结合我国目前的汽车保有量及近年来二手车市场发展现状,分析目前我国二手车市场的特点,对于未来二手车的前景进行分析。     

浅谈二手车及其鉴定评估方法

中国是世界上汽车产销量最大的国家,随着汽车存有量不断攀升、汽车消费文化的提升,汽车消费环境的不断优化,二手车的消费将会成为汽车消费的重要支撑,而在二手车消费的过程中,就需要对二手车的价值进行科学有效的评估。     

浅析汽车维修新技术中发动机故障检测及维修技术

随着我国社会发展进步,汽车已经成为人民生活中不可或缺的一部分,而汽车运行过程中难免会发生故障,其中发动机故障的影响尤为严重。从简述汽车维修新技术的重要作用入手,进一步分析汽车发动机故障的检测方法,探究汽车维修新技术在发动机异响、发动机油样、发动机启停及发动机冷却系统故障检测和维修的途径。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Modeling the interactions between car and bicycle in heterogeneous traffic

In this paper, a new cellular automata model is proposed to simulate the car and bicycle heterogeneous traffic on urban road. To capture the complex interactions between these two types of vehicles, a novel occupancy rule is adopted in the proposed model to consider the variable lateral distances of mixed vehicular traffic. Based on massive simulations, microscopic fundamental diagrams under different bicycle densities are devised. With these, the bicycle's spilling behavior is then investigated and discussed. In order to reflect the interference of a bicycle on a car, the interference transformation from friction state to block state is modeled explicitly. Finally, different simulation results under different occupancy rules indicate that the constant and fixed occupancy rule adopted in the previous studies might lead to overestimation of car flux in the heterogeneous traffic flows with different bicycle densities. Copyright © 2013 John Wiley & Sons, Ltd.     

奥迪100轿车后螺旋弹簧的可靠性设计

本文介绍了奥迪１００桥车后螺旋弹簧的可靠性设计方法。在基本随机变量的概率特性已知的情况下，使用二阶矩技术对弹簧的可靠性进行设计，所编制的计算机程序可以迅速准确地得到弹簧的可靠性设计参数。     

虚拟三维环境中汽车模型的照明设置

通过在虚拟三维环境中怎样对汽车模型进行灯光设置的研究,提出车身金属材质的具体设置方法,以及对虚拟场景中灯光的设置,以达到显示其形状、材质、气势等效果,追求虚拟三维汽车模型的艺术效果。