The status, challenges, and trends: an interpretation of technology roadmap of intelligent and connected vehicles in China (2020)

Xu, Qing; Li, Keqiang; Wang, Jianqiang; Yuan, Quan; Yang, Yanding; Chu, Wenbo

doi:10.1108/jicv-07-2021-0010

Cited by 33 publications

(10 citation statements)

References 6 publications

(7 reference statements)

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“…The latest generation of vehicle dynamics controllers, including stability, traction, active steering, and active suspension controllers, as well as automated driving systems, demands increasing amounts of data from the vehicle and its surroundings [ 1 , 2 , 3 , 4 , 5 ]. However, specific vehicle variables, such as the sideslip angle, tyre slip ratios and slip angles, vehicle speed, and tyre–road friction level, cannot be robustly and cost-effectively measured for mass production implementations, and therefore must be estimated.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Alshawi,

De Pinto,

Stano

et al. 2024

Sensors

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This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Alshawi,

De Pinto,

Stano

et al. 2024

Sensors

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show abstract

“…However, it has been recognized that a long transition period is required that entails vigorous development of cooperative vehicle‐infrastructure system (CVIS) before CAV substantially penetrate the market (Xu, Li, et al., 2022). An essential role of CVIS is capturing motion data of multiple agents on the roads, which serve as the primary inputs for intelligent traffic management and control systems (Chen, Dong, et al., 2021; Shi et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Ma,

Zheng,

Wang

et al. 2024

Computer aided Civil Eng

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Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real‐life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual‐real‐fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real‐world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure‐retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM‐based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM‐based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM‐based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches.

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“…The deployment of connectivity technology in vehicles, particularly in urban regions, continues to be limited (Dong et al., 2021; Montanaro et al., 2019; Xu et al., 2022). This makes it challenging to implement dynamic rerouting in most metropolitan areas.…”

Section: Introductionmentioning

confidence: 99%

Dynamic urban traffic rerouting with fog‐cloud reinforcement learning

Du,

Chen,

Dong

et al. 2023

Computer aided Civil Eng

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Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its implementation is stymied by the complexity of urban traffic. To address this, recent studies suggest the efficacy of novel technologies like fog computing and deep reinforcement learning. However, there exist significant challenges in this regard: (1) sorting massive amounts of data associated with large urban networks, (2) large action space that hinders learning efficiency, (3) impairment of rerouting efficacy due to overreliance on regional/local information, and (4) the issue of congestion shifting. To overcome these challenges, this paper presents a novel two‐step approach that integrates graph attention Q network (GAQ) with entropy‐balanced k shortest path (EBkSP) via fog‐cloud information framework to carry out vehicle rerouting in dynamic traffic environments typical of large cities. The first challenge is addressed by GAQ that uses attention mechanism to evaluate traffic and assign region indices based on the relative importance of information. The second challenge is addressed using fog nodes that reduce the action space, and the third is addressed using fog computing combined with cloud computing to facilitate the computation of global optimal information in a centralized planning decentralized execution pattern. The fourth challenge is addressed using EBkSP, which identifies vehicle optimal routes based on route popularity and vehicle priority. After conducting a numerical experiment involving two other cutting‐edge vehicle rerouting models as baselines, it was established that the proposed model delivers results that are 14%–54% superior from perspectives of the mean travel speed and congestion. The superior efficacy of the proposed learning‐based (compared to rule‐based) models is pronounced when rerouting ratios are low. As the rerouting ratio increases, both the learning‐based and rule‐based model outcomes exhibit reduced likelihoods of severe congestion. However, the learning‐based model consistently outperforms the rule‐based model across all scenarios. The proposed model can effectively reroute vehicles under different situations of rerouting ratio and total count of vehicles and can help improve traffic mobility and reduce congestion in urban areas. It can be implemented easily by urban road agencies.

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The status, challenges, and trends: an interpretation of technology roadmap of intelligent and connected vehicles in China (2020)

Cited by 33 publications

References 6 publications

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Dynamic urban traffic rerouting with fog‐cloud reinforcement learning

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