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学者姓名：林典超
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Abstract ：
With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. Themechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.
Keyword ：
Car-following model Car-following model communication delay communication delay connected automated vehicle connected automated vehicle discrete signal discrete signal hard brake hard brake mechanical delay mechanical delay packet loss packet loss safety constraint safety constraint
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| GB/T 7714 | Lin, DianChao , Li, Li . An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2023 , 72 (8) : 9783-9795 . |
| MLA | Lin, DianChao 等. "An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 72 . 8 (2023) : 9783-9795 . |
| APA | Lin, DianChao , Li, Li . An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2023 , 72 (8) , 9783-9795 . |
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Imminent Throughput (ITP), the number of vehicles in a movement that can pass through an intersection given a unit of effective green time, serves as a crucial control input in many real-time optimization-based control schemes. The accuracy of ITP prediction can significantly influence control performance. If a movement with a green signal cannot discharge as many vehicles as predicted by the controller, its performance may be significantly reduced. However, most existing studies have focused on the design of control schemes while neglecting the importance of precise ITP prediction. These studies either assume that ITP can be accurately predicted or use traditional indices (e.g., saturation flow rate) or heuristic methods to predict ITP, resulting in relatively low accuracy. This paper proposes the use of a Deep Neural Network (DNN) to predict ITP and demonstrates that the DNN with Multiple Classifications (NN-C) models can predict ITP with higher accuracy, lower mean absolute error, and lower root mean squared error than other prediction methods (regression, decision tree, and heuristic methods). Experiments also show that control performance can be improved with more accurate ITP predictions using the NN-C. © 2023 IEEE.
Keyword ：
Decision trees Decision trees Deep neural networks Deep neural networks Forecasting Forecasting Heuristic methods Heuristic methods Mean square error Mean square error
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| GB/T 7714 | Lin, Dianchao , Li, Li , Xue, Nian et al. Learning Imminent Throughput for Real-time Intersection Control with Deep Neural Network [C] . 2023 : 67-72 . |
| MLA | Lin, Dianchao et al. "Learning Imminent Throughput for Real-time Intersection Control with Deep Neural Network" . (2023) : 67-72 . |
| APA | Lin, Dianchao , Li, Li , Xue, Nian , Wang, Lei . Learning Imminent Throughput for Real-time Intersection Control with Deep Neural Network . (2023) : 67-72 . |
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New technologies present opportunities to re-think the real-time traffic operation. Trade of road priority using schemes such as auctions, credits, and direct-transactions, provides a novel way to better serve heterogeneous traffic demands in the future connected environment. Implementations of these schemes come with a common phenomenon called the network effect, meaning that the value of the scheme depends heavily on the percentage of travelers that subscribe to this scheme. To encourage travelers to be subscribers, the traffic operator needs to reasonably consider the treatments for both subscribers and outsiders, and have the knowledge of each scheme's impact on individuals and society. Only in this way can the government choose an appropriate scheme that will be accepted by the public. However, most existing studies simply assumed a 100% of subscribers and ignored the travelers' autonomy and smartness in choice behavior. Such neglect may easily render the economic schemes failed in practice due to public resistance or loss in subscribers. To fill up this research gap, this paper designs treatments to both subscribers and outsiders for different schemes in intersection operations. We investigate travelers' choice behaviors under different scenarios, and make comparative analysis about individual benefits, social benefit, and social equity between different schemes. The findings of this paper can support the government's decision on the system design of road priority trading.
Keyword ：
Auction Auction benefit benefit comparative analysis comparative analysis Costs Costs credit scheme credit scheme direct-transaction direct-transaction economic schemes economic schemes equity equity Government Government Licenses Licenses network effect network effect Real-time systems Real-time systems Resistance Resistance Roads Roads subscribing choice subscribing choice trade of road priority trade of road priority Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Li, Li , Lin, Dianchao . Design and Comparative Analysis on Real-Time Trade of Road Priority in Connected Traffic [J]. | IEEE ACCESS , 2022 , 10 : 52210-52222 . |
| MLA | Li, Li et al. "Design and Comparative Analysis on Real-Time Trade of Road Priority in Connected Traffic" . | IEEE ACCESS 10 (2022) : 52210-52222 . |
| APA | Li, Li , Lin, Dianchao . Design and Comparative Analysis on Real-Time Trade of Road Priority in Connected Traffic . | IEEE ACCESS , 2022 , 10 , 52210-52222 . |
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The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle's data and ignored other vehicles' data. Based on those, this article designs a new MM method to make full use of "big data." We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is <= 0.01Hz.
Keyword ：
Artificial neural networks Artificial neural networks Collaboration Collaboration Data mining Data mining Global navigation satellite system Global navigation satellite system Probes Probes Satellites Satellites Trajectory Trajectory
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| GB/T 7714 | Fang, Jie , Wu, Xiongwei , Lin, Dianchao et al. A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2022 . |
| MLA | Fang, Jie et al. "A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2022) . |
| APA | Fang, Jie , Wu, Xiongwei , Lin, Dianchao , Xu, Mengyun , Wu, Huahua , Wu, Xuesong et al. A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2022 . |
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The rapid development of new technologies, e.g., connected vehicles and e-wallet, offers us opportunities to rethink cooperative lane-change (LC) models. Most of existing cooperative LC models require a selfless assumption by default, which is usually too stringent for travelers. A cooperative LC game framework with appropriate side-payments can make up for this deficiency. In such games, when travelers maximize their own benefits, they will naturally cooperate to improve the system performance. This paper designs a four-step transferable utility based three-player game framework, which can cover all LC scenarios on multi-lane roads combing with the two-player model in the previous work. Our four-step framework is also suitable for other cooperative games with side-payments. Furthermore, the simulation results show that, cooperative games with side-payments can reduce vehicles' LC frequency and meanwhile benefit all transaction vehicles in most scenarios in expectation. Moreover, the urgent vehicles with very high values of time can save up to 42% travel time on congested roads under the simulation settings.
Keyword ：
Accidents Accidents Biological system modeling Biological system modeling Connected vehicles Connected vehicles cooperative game cooperative game direct-transaction direct-transaction discretionary lane change discretionary lane change Games Games Real-time systems Real-time systems Roads Roads side-payments side-payments three players three players time saving time saving transferable utility transferable utility TV TV value of time value of time win-win solution win-win solution
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| GB/T 7714 | Li, Li , Lin, Dianchao . Three-Player Cooperative Game With Side-Payments for Discretionary Lane Changes of Connected Vehicles [J]. | IEEE ACCESS , 2021 , 9 : 159848-159857 . |
| MLA | Li, Li et al. "Three-Player Cooperative Game With Side-Payments for Discretionary Lane Changes of Connected Vehicles" . | IEEE ACCESS 9 (2021) : 159848-159857 . |
| APA | Li, Li , Lin, Dianchao . Three-Player Cooperative Game With Side-Payments for Discretionary Lane Changes of Connected Vehicles . | IEEE ACCESS , 2021 , 9 , 159848-159857 . |
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