A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model.

For identifying the optimal model for real-time conflict prediction, there is a necessity for proposing a quantitative analysis approach that adaptively selects the optimal prediction model from a large pool of task-suited models, while simultaneously considering the computational efficiency and prediction precision. Based on this line, this study developed an innovative approach termed surrogate model-based optimal prediction model selection (SM-OPMS). This approach aims to accelerate the optimal model selection while incorporating prediction precision considerations, under the precondition of comprehensively evaluating task-suited models. An analytical framework was proposed, further illustrated through a detailed case study. In the case study, real vehicle trajectory data from HighD were processed and applied, which can be aggregated to extract both traffic state variables and corresponding conflict data during a specific time interval. As for the conflict detection, Time-to-Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) indicators were utilized to identify risky conditions. Based on the proposed approach, the selection for the optimal prediction model was conducted, and the variable importance in conflict prediction within the optimal models derived from the SM-OPMS was also investigated. Finally, a comparative analysis with the enumeration-based optimal prediction model selection (E-OPMS) approach was conducted to validate the superiority of the proposed approach. Results indicate that SM-OPMS outperforms E-OPMS in optimal model selection, notably enhancing computational efficiency by up to 94.03%, while maintaining prediction precision within a maximum reduction of only 7.91%. The significance of the SM-OPMS approach is revealed by its comprehensive selection of the optimal prediction models for specific traffic scenarios, taking into account both prediction efficiency and precision simultaneously. The proposed approach is expected to contribute to the development of real-time conflict prediction in the future.

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected to capture within-group and between-group characteristics of the stages. Besides, this study also examines the prediction performance of conflict stage identification using specific driving behaviour characteristics associated with each stage. Results reveal variations in dominant driving characteristics and predictive importance across stages. Heterogeneity exists within stages, with differences among clusters. Drivers slow down during in-conflict, with decreasing speed reduction as stages progress. Reaction time increases in post-conflict. Insufficient space gaps contribute to rear-end conflicts in the in-conflict stage. Furthermore, the prediction performance of conflict stage identification, based on the specific driving behaviour characteristics associated with each stage, is commendable. This study enhances understanding and prediction of conflict stage identification in rear-end conflicts.Practitioner summary: This study explores driving behaviour in rear-end conflict stages using trajectory data. It identifies pre-, in-, and post-conflict stages via time-to-collision indicator and assesses within-group and between-group characteristics. Besides, prediction performance for conflict stage identification based on these characteristics is commendable. This research enhances understanding and prediction of rear-end conflicts.

Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.