Charting the future:Intelligent and connected vehicles reshaping the bus system

1 Introduction Driven by technological innovation and digital evolution,the current automotive industry is standing at the cusp of a transformative era(Liu et al.,2023).As urban centers continue to expand and intensify the demands on transportation networks,the need for solutions to alleviate congestion,boost traffic efficiency,and enhance road safety becomes increasingly urgent.On this occasion,intelligent and connected vehicles,integrating vehicles,infrastructure,and cloud computing,promise a smarter mode of passenger transportation and pave the way for a more interconnected and responsive urban transit ecosystem(Cao et al.,2023).Therefore,traditional passenger buses are on the verge of significant transformation in terms of their functional technologies and operational models.This will bring about a host of benefits such as higher efficiency,better passenger experiences,and safer road environments.This paper provides a comprehensive outlook on intelligent and connected passenger buses(ICPBs),delving into the integrated vehicle-road-cloud platform and highlighting the key technologies that will shape the future bus system.As illustrated in Fig.1,it showcases the key perspectives on the future of ICPBs.

A deep learning method for traffic light status recognition

Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.