A Car-Following Model Based on Quantified Homeostatic Risk Perception

Lu, Guangquan; Cheng, Bo; Wang, Yunpeng; Lin, Qingfeng

doi:10.1155/2013/408756

Cited by 29 publications

(19 citation statements)

References 44 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of risk homeostasis theory (RHT) theory and the stimulus-response concept, Lu et al [12], [13] proposed a desired safety margin (DSM) model, which gives a new way to explain car-following process. To make safety algorithms better adapted to the driver's behaviors, a self-learning algorithm for driver characteristics was proposed by Tsinghua University based on the recursive least-square method with a forgetting factor, which was used in an adaptive longitudinal driver assistance system [14].…”

Section: Introductionmentioning

confidence: 99%

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Wang

2015

IEEE Trans. Intell. Transport. Syst.

255

View full text Add to dashboard Cite

Vehicle driving safety is influenced by many factors, including drivers, vehicles, and road environments. The interactions among them are quite complex. Consequently, existing methods that evaluate driving safety perform inadequately because they only consider limited factors and their interactions. As such, it is difficult for kinematics-based and dynamics-based vehicle driving safety assistant systems to adapt to increasingly complex traffic environments. In this paper, we propose a new concept, i.e., the driving safety field. The concept makes use of field theory to represent risk factors owing to drivers, vehicles, road conditions, and other traffic factors. A unified model of the driving safety field is constructed, which includes the following three parts: 1) a potential field, which is determined by nonmoving objects on the roads, such as a stopped vehicle; 2) a kinetic field, which is determined by the moving objects on roads, such as vehicles and pedestrians; and 3) a behavior field, which is determined by the individual characteristics of drivers. Moreover, the applications of the model are proposed, and its application to a typical carfollowing scenario is illustrated, which evaluates the risks caused by multiple traffic factors. The driving safety field can reveal driver-vehicle-road interactions and their influences on driving safety, as well as predict driving safety trends owing to dynamic changes. In addition, the model can provide a new foundation for establishing driving safety measures and active vehicle control under complex traffic environments.

show abstract

Section: Introductionmentioning

confidence: 99%

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Wang

2015

IEEE Trans. Intell. Transport. Syst.

255

View full text Add to dashboard Cite

show abstract

“…This is analogous to the results in [38][39][40], that is, distance headway increases as the host vehicle speed increases and time headway is nearly constant in different speed ranges. However, time headway is described to decrease as the host vehicle speed increases in [22], [41,42]. This may has some relationship with the congestion conditions in [41], simulation conditions in [42], and that there were only a few data points in [22].…”

Section: Comparison Of Ttc and Time Gap In Various Relative Speed mentioning

confidence: 99%

“…However, time headway is described to decrease as the host vehicle speed increases in [22], [41,42]. This may has some relationship with the congestion conditions in [41], simulation conditions in [42], and that there were only a few data points in [22].…”

Section: Comparison Of Ttc and Time Gap In Various Relative Speed mentioning

confidence: 99%

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Tong

Selpi

2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Understanding car-following behaviour in different countries is essential for the design and development of autonomous driving and further development of active safety systems that can function well worldwide, in particular in mixed traffic conditions. However, very few studies exist that compare car-following behaviours in different countries based on real driving data. This paper analyses the similarities and differences of drivers' car-following behaviour, in terms of time gap, gap distance, and time to collision, using both China and Sweden datasets from real road driving studies, in a bid to identify how these indicators affect drivers' speed control in car-following situations. Results indicate that the highest frequency of gap distance is observed in the same value-range in both datasets, while the highest frequency of time gap in the Sweden dataset is found at a lower value-range than the corresponding value-range in the China dataset. For both datasets, time gap is observed to be a more reliable indicator for car-following analysis than gap distance, since it is less sensitive to speed variations. Furthermore, time to collision (TTC) in the low travel speed ranges (v<50 km/h) tends to be steady in comparison with TTC at other speed ranges, so is the time gap in the high speed ranges (v>90 km/h). Therefore, time gap is recommended as the safety indicator for car-following analysis in high speed conditions, while a combination of time gap and TTC is recommended for low speed conditions, especially on urban roads.

show abstract

“…Lu et al [32] obtained statistical information on driver reaction time through 63 samples, as shown in Table 4.…”

Section: Driver Reaction Timementioning

confidence: 99%

The Driver Time Memory Car-Following Model Simulating in Apollo Platform with GRU and Real Road Traffic Data

Fei

Hei

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Car following is the most common phenomenon in single-lane traffic. The accuracy of acceleration prediction can be effectively improved by the driver’s memory in car-following behaviour. In addition, the Apollo autonomous driving platform launched by Baidu Inc. provides fast test vehicle following vehicle models. Therefore, this paper proposes a car-following model (CFDT) with driver time memory based on real-world traffic data. The CFDT model is firstly constructed by embedded gantry control unit storage capacity (GRU assisted) network. Secondly, the NGSIM dataset will be used to obtain the tracking data of small vehicles with similar driving behaviours from the common real road vehicle driving tracks for data preprocessing according to the response time of drivers. Then, the model is calibrated to obtain the driver’s driving memory and the optimal parameters of the model and structure. Finally, the Apollo simulation platform with high-speed automatic driving technology is used for 3D visualization interface verification. Comparative experiments on vehicle tracking characteristics show that the CFDT model is effective and robust, which improves the simulation accuracy. Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model.

show abstract

A Car-Following Model Based on Quantified Homeostatic Risk Perception

Cited by 29 publications

References 44 publications

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

The Driver Time Memory Car-Following Model Simulating in Apollo Platform with GRU and Real Road Traffic Data

Contact Info

Product

Resources

About