| 1. |
- Gao, Jianqiang, et al.
(författare)
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An ADAS with better driver satisfaction under rear-end near-crash scenarios: A spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk
- 2024
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 159
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Tidskriftsartikel (refereegranskat)abstract
- Current advanced driver assistance systems (ADASs) do not consider drivers’ preferences of evasive behavior types and risk levels under rear-end near-crash scenarios, which undermines driver satisfaction, trust, and use of ADASs. Additionally, spatio-temporal interactions between vehicles are not fully involved in current evasive behavior prediction models, and the influence of evasive behavior is ignored while predicting collision risk. To address these issues, this study aims to propose an ADAS with better driver satisfaction under rear-end near-crash scenarios by establishing a spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk. A total of 822 evasive events are extracted from 108,000 real vehicle trajectories on highways, and variables from three sources (i.e., road environment features, evading vehicle features, and interactive behavior features) are used to construct rear-end near-crash scenario knowledge graphs (RNSKGs). By utilizing RNSKGs embedding and multi-head self-attention mechanism, spatio-temporal graph transformer networks can effectively capture the spatio-temporal interactions between vehicles. The results show that the prediction accuracy of evasive behavior (i.e., braking-only or braking and steering) and collision risk (lower, medium, or higher risk) is 96.34% and 92.12%, respectively, superior to other commonly-used methods. After including the selected evasive behavior in predicting collision risk, the overall accuracy increases by 10.91%. Then, an autonomous evasive takeover system (AET) based on the prediction framework is developed, and its effectiveness and satisfaction are verified by driving simulation experiments. According to the self-reported data of participants, the safety, comfort, usability, and acceptability of AET proposed in this study all significantly outperform existing autonomous takeover systems (i.e., autonomous emergency braking and autonomous emergency steering). The findings of this study might contribute to the optimization of ADASs, the enhancement of mutual understanding between ADASs and human drivers, and the improvement of active driving safety.
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| 2. |
- Genze, Li, et al.
(författare)
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A Novel Longitudinal Control Strategy of Connected Automated Vehicle in Heterogeneous Traffic Flow and String Stability Analysis
- 2023
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Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 149-160
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Konferensbidrag (refereegranskat)abstract
- To cope with the randomness derived from the human driving in heterogeneous traffic consists of human-driving vehicles and connected automated vehicles (CAVS), a longitudinal car-following control strategy of CAV is proposed based on the original Intelligent Driver Model (IDM) model and model predictive control (MPC) structure. The string stability of heterogeneous platoon is verified by head-to-tail string stability criteria. Results indicate the strategy proposed can reflect the relationship between the speed and string stability and prove the adaptability to different traffic conditions.
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| 3. |
- Wang, Shuli, 1996, et al.
(författare)
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Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
- 2024
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Ingår i: Accident Analysis and Prevention. - 0001-4575. ; 199
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Tidskriftsartikel (refereegranskat)abstract
- Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.
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| 4. |
- Wang, Shuli, 1996, et al.
(författare)
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Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data
- 2023
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Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 45-54
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Konferensbidrag (refereegranskat)abstract
- Accurate driving preferences classification is a crucial component for autonomous connected vehicles in making more safety and more efficient driving decisions. Most existing studies identify drivers’ driving preferences based on the historical data of the individual vehicle, and the selected variables are limited to the mechanical motion of the vehicle, which seldomly takes the influence of road traffic conditions and surrounding vehicles into account. This study proposes a driving preferences classification method by multivariate sequence clustering algorithm based on wide-range trajectory data. Based on the specific range of road sections, the selected variables for each trajectory are converted from the time domain to the space domain separately, to capture the dynamic changes of the features along the road area. Multivariate time series clustering combining a weighted Dynamic Time Warping (WDTW) and the k-medoids algorithm is used to classify driving preferences into different levels, and a popular internal evaluation metric is employed to determine the optimal cluster result. This study also investigates the heterogeneity of driving behaviors at different driving preference levels. The results show that the proposed method could better recognize drivers’ internal driving preferences.
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| 5. |
- Wang, Shuli, 1996, et al.
(författare)
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Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity With Interpretability
- 2024
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- To promise a high degree of safety in complex mixed-traffic scenarios alongside human-driven vehicles, accurately predicting the maneuvers of surrounding vehicles and their future positions is a critical task and attracts much attention. However, most existing studies focus on reasoning about positional information based on objective historical trajectories without fully considering the heterogeneity of driving behaviors. Besides, previous works have focused more on improving models’ accuracy than investigating their interpretability to explore the extent to which a cause and effect can be observed within a system. Therefore, this article proposes a personalized trajectory prediction framework that integrates driving behavior feature representation to account for driver heterogeneity. Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers. The proposed LSTMMD-DBV (long short-term memory and mixture density networks with driving behavior vectors) framework integrates driving behavior feature representations into a long short-term memory encoder–decoder network to investigate its feasibility and validate its effectiveness in enhancing predictive model performance. Finally, the Shapley Additive Explanations method interprets the trained model for predictions. After experimental analysis, the results indicate that the proposed model can generate probabilistic future trajectories with remarkably improved predictions compared to existing benchmark models. Moreover, the results confirm that the additional input of driving behavior feature vectors representing the heterogeneity of driving behavior could provide more information and, thus, contribute to improving prediction accuracy.
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