These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
142 related articles for article (PubMed ID: 36081222)
1. Efficiency performance and safety evaluation of the responsibility-sensitive safety in freeway car-following scenarios using automated longitudinal controls. Hassanin O; Wang X; Wu X; Xu X Accid Anal Prev; 2022 Nov; 177():106799. PubMed ID: 36081222 [TBL] [Abstract][Full Text] [Related]
2. Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data. Zhou W; Wang X; Glaser Y; Wu X; Xu X Accid Anal Prev; 2022 Dec; 178():106834. PubMed ID: 36150234 [TBL] [Abstract][Full Text] [Related]
3. Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model. Wang X; Ye C; Quddus M; Morris A Accid Anal Prev; 2023 Nov; 192():107265. PubMed ID: 37619318 [TBL] [Abstract][Full Text] [Related]
4. Optimal jam-absorption driving strategy for mitigating rear-end collision risks with oscillations on freeway straight segments. Zheng Y; Zhang G; Li Y; Li Z Accid Anal Prev; 2020 Feb; 135():105367. PubMed ID: 31813474 [TBL] [Abstract][Full Text] [Related]
5. Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study. Chen R; Kusano KD; Gabler HC Traffic Inj Prev; 2015; 16 Suppl 2():S176-81. PubMed ID: 26436229 [TBL] [Abstract][Full Text] [Related]
6. Human-like car-following model for autonomous vehicles considering the cut-in behavior of other vehicles in mixed traffic. Fu R; Li Z; Sun Q; Wang C Accid Anal Prev; 2019 Nov; 132():105260. PubMed ID: 31442924 [TBL] [Abstract][Full Text] [Related]
7. Velocity control in car-following behavior with autonomous vehicles using reinforcement learning. Wang Z; Huang H; Tang J; Meng X; Hu L Accid Anal Prev; 2022 Sep; 174():106729. PubMed ID: 35700685 [TBL] [Abstract][Full Text] [Related]
8. Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles. Mahdinia I; Mohammadnazar A; Arvin R; Khattak AJ Accid Anal Prev; 2021 Mar; 152():106006. PubMed ID: 33556655 [TBL] [Abstract][Full Text] [Related]
9. An optimal control-based vehicle speed guidance strategy to improve traffic safety and efficiency against freeway jam waves. Han Y; Yu H; Li Z; Xu C; Ji Y; Liu P Accid Anal Prev; 2021 Dec; 163():106429. PubMed ID: 34638010 [TBL] [Abstract][Full Text] [Related]
10. Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset. Wen X; Cui Z; Jian S Accid Anal Prev; 2022 Jul; 172():106689. PubMed ID: 35569279 [TBL] [Abstract][Full Text] [Related]
11. Age and gender differences in time to collision at braking from the 100-Car Naturalistic Driving Study. Montgomery J; Kusano KD; Gabler HC Traffic Inj Prev; 2014; 15 Suppl 1():S15-20. PubMed ID: 25307380 [TBL] [Abstract][Full Text] [Related]
12. Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study. Wang X; Zhang X; Guo F; Gu Y; Zhu X Accid Anal Prev; 2022 Jan; 164():106502. PubMed ID: 34837850 [TBL] [Abstract][Full Text] [Related]
13. Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions. Mattas K; Albano G; DonĂ R; Galassi MC; Suarez-Bertoa R; Vass S; Ciuffo B Accid Anal Prev; 2022 Sep; 174():106743. PubMed ID: 35700684 [TBL] [Abstract][Full Text] [Related]
14. Fuzzy Surrogate Safety Metrics for real-time assessment of rear-end collision risk. A study based on empirical observations. Mattas K; Makridis M; Botzoris G; Kriston A; Minarini F; Papadopoulos B; Re F; Rognelund G; Ciuffo B Accid Anal Prev; 2020 Dec; 148():105794. PubMed ID: 33032008 [TBL] [Abstract][Full Text] [Related]
15. Effects of peripheral transverse line markings on drivers' speed and headway choice and crash risk in car-following: A naturalistic observation study. Ding N; Zhu S; Jiao N; Liu B Accid Anal Prev; 2020 Oct; 146():105701. PubMed ID: 32823033 [TBL] [Abstract][Full Text] [Related]
16. Population distributions of time to collision at brake application during car following from naturalistic driving data. Kusano KD; Chen R; Montgomery J; Gabler HC J Safety Res; 2015 Sep; 54():95-104. PubMed ID: 26403908 [TBL] [Abstract][Full Text] [Related]
17. Analysis of car driver responses to avoid car-to-cyclist perpendicular collisions based on drive recorder data and driving simulator experiments. Zhao Y; Miyahara T; Mizuno K; Ito D; Han Y Accid Anal Prev; 2021 Feb; 150():105862. PubMed ID: 33276185 [TBL] [Abstract][Full Text] [Related]
18. How do drivers respond to driving risk during car-following? Risk-response driver model and its application in human-like longitudinal control. Zhao X; He R; Wang J Accid Anal Prev; 2020 Dec; 148():105783. PubMed ID: 33022511 [TBL] [Abstract][Full Text] [Related]
19. RSS Model Improvement Considering Road Conditions for the Application of a Variable Focus Function Camera. Kim MJ; Kim YM Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679387 [TBL] [Abstract][Full Text] [Related]
20. Optical information for car following: the driving by visual angle (DVA) model. Andersen GJ; Sauer CW Hum Factors; 2007 Oct; 49(5):878-96. PubMed ID: 17915604 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]