Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Secret key distribution leveraging color shift over visible light channel

Given the widely adoption of screen and camera in many electronic devices, the visible light communication (VLC) over screen-to-camera channel emerges as a novel short range communication technique in recent years. Active research explores various ways to convey messages over screen-camera channel, such as barcode and unobtrusive optical pattern. However, with the prevalence of LED screens of wide viewing angles and mobile devices equipped with high standard cameras, the threat of information leakage over screen-to-camera channel becomes in-negligible. Few studies have discussed how to ensure the security of data transmission over screen-to-camera channel. In this paper, we propose a secret key distribution system leveraging the unique color shift property over visible light channel. To facilitate such design, we develop a practical secret key matching based method to map the secret key into gridded optical patterns on screen, which can only be correctly recognized by the legitimate user through an accessible region and allow regular data stream transmission through valid grids. The proposed system is prototyped with off-the-shelf devices and validated under various experimental scenarios. The results show that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining comparable data throughput as regular unobtrusive VLC systems with very low recovery accuracy of the encrypted data for the attackers

Driving forces of CO2 emissions and mitigation strategies of China’s National low carbon pilot industrial parks

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO2 emissions. The elasticity of energy intensity and renewable energy consumption on CO2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO2 emissions are also provided