CAVTest: A Closed Connected and Automated Vehicles Test Field of
                    Chang’an University in China

Wang, Runmin; Zhao, Xiangmo; Xu, Zhihong; Yang, Yipeng

doi:10.4271/12-04-04-0032

Cited by 8 publications

(6 citation statements)

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“…Finally, the Dijkstra algorithm is employed to obtain the optimal matching path sequence based on these weights [39]. A researcher used the topological optimization based on the speed constraint (SC + TO) method combined with the technique for order preference by similarity to the ideal solution method (TOPSIS) to complete the extraction and reconstruction of vehicle travel paths [40]. The study constructed the feasible solution set of travel paths based on the K-th shortest paths algorithms (KSP).…”

Section: Lbs Data Completionmentioning

confidence: 99%

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

Wang,

Shi,

Chen

et al. 2024

Applied Sciences

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The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

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Section: Lbs Data Completionmentioning

confidence: 99%

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

Wang,

Shi,

Chen

et al. 2024

Applied Sciences

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“…The experiment methods for pedestrian–vehicle interaction behaviour mainly include driving simulator experiments with/without virtual reality (VR)-based devices [ 3 , 7 , 8 , 9 , 10 ], designed experiments in closed field scenes [ 11 , 12 ], and naturalistic driving study (NDS) in open roads scenes [ 13 , 14 , 15 ]. Although the NDS has the disadvantage of efficiency and security compared to the other two methods due to the accidental and uncontrollable interaction environments, it can reflect the real behaviour of road users, which cannot be affected by experiment conditions and related deviations [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

Sun

Zhang

et al. 2022

Sensors

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One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

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“…At the same time, many closed CAVs test (CAVTest) fields have realized key test elements for CAV functions. Taking the closed CAVTest of Chang'an University in China [9] as an example, the critical test elements generally include a variety of road environments (cities, highways, and rural roads), environmental complexity (weather, emergencies), road infrastructure (road signs and markings, traffic control facilities, wading roads), auxiliary test equipment (dynamic and static obstacles), and network conditions (RSU, communication protocols, lidars). However, the traffic operating and CAV working conditions are inextricably linked, and a single evaluation for any side will inevitably fall into the problem because of the light evaluation mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…However, IVIS is not only a simple superposition of road systems and CAV functions but also an organic fusion of traffic engineering, information technology, and the automotive industry, having the characteristics of multisystem cooperation and with specific functions corresponding to particular needs. On the other hand, in the current closed CAVTest fields, the test scenario is generally for a single refined scenario, such as the communication function test for LET-V, and AEB for the pedestrian test [9]. The IVIS technology in mass production faces comprehensive and complex scenarios, such as the high-speed on-ramp merging scenario, which involves communication, cut-in, obstacle recognition, and reaction and other functions, which have seldom been discussed in most of the existing studies.…”

Section: Introductionmentioning

confidence: 99%

“…Due to article space limitations, we only focused on the selections of the IVIS integration test evaluation index under closed conditions. The closed condition refers to the conditions in which the IVIS elements are repeatable and steerable or are not affected by external factors [9]. As shown in Figure 1, IVIS testing under closed conditions should be executed in two parts: one is to meet the stress test (subdivided into industry development demand, technical test demand, and response testing demand), while the other is to match the strength test (subdivided into extreme weather testing demand, extreme road conditions demand, and extreme communication test demand) [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Index for IVIS Integration Test under a Closed Condition Based on the Analytic Hierarchy Process

Chen

et al. 2022

Electronics

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The intelligent vehicle infrastructure system (IVIS) requires systematic testing before being put into large-scale applications. IVIS testing under closed conditions includes stress tests for typical scenarios and extreme scenario strength testing. To extract IVIS integration test indicators under closed conditions, this article constructed a hierarchical framework of IVIS’s evaluation indexes in the stress tests and the strength tests. The hierarchical framework of IVIS stress test evaluation indicators reflect the highway construction area under typical scenarios, and the hierarchical framework of IVIS strength test evaluation indicators reflect the highway merging area under extreme scenarios. Both are based on the test requirements of the stress test and strength test, with safety as the evaluation objective. Second, the analytic hierarchy process (AHP) was used to calculate the weights of the test evaluation indicators of the two scenarios. Finally, the activity-based classification (ABC) method was used after ranking the weight results in order to extract the key factors that have the maximum impact on safety in the scenarios. In this paper, we proved the practicality and feasibility of the AHP-ABC extraction method in the IVIS integration testing evaluation index and guided the development and testing of the IVIS.

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CAVTest: A Closed Connected and Automated Vehicles Test Field of Chang’an University in China

Cited by 8 publications

References 0 publications

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study

Evaluation Index for IVIS Integration Test under a Closed Condition Based on the Analytic Hierarchy Process

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