Findings on the Approach Process Between Vehicles: Implications for Collision Warning

Author(s):  
Mark A. Brackstone ◽  
Beshr Sultan ◽  
Michael McDonald

Over the past 10 years there has been a growing body of research into modeling and describing driving behavior, particularly for situations that occur on motorways. This interest has arisen from the need to assess safety and capacity benefits that could be produced by changes to road design, operation, signage, and in-vehicle advanced transport telematics, such as collision warning (CW) or autonomous cruise control. For the most part these investigations have focused on “close” or “car” following, which describes the maintenance of a time- or distance-based following headway. However, often overlooked, and of equal importance, is the “approach” process, describing how a driver decelerates when approaching a slower vehicle. There are several competing theories of the behavioral basis underlying this process, including, for example, those based on time-to-collision or optic flow. There are, however, very few data against which such models can be assessed and systems designed. Presented are the results from an exploratory, instrumented vehicle study designed to assess approach mechanisms. The two key features of the process are explored: the circumstances under which driver deceleration is instigated, and the process governing the control of the deceleration itself. Finally, there is a brief assessment of the implications of these findings for the design of CW systems, in which realistic warnings may prove vital to their acceptance by the driving public.

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6376
Author(s):  
Haksu Kim ◽  
Kyunghan Min ◽  
Myoungho Sunwoo

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.


2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jiangfeng Wang ◽  
Xuedong Yan ◽  
Shuo Nie ◽  
Xiaomeng Li

Using inter-vehicle communication, vehicle real-time traveling state information, such as acceleration, is shared, and time-to-collision and time-to-avoidance matrices are established. Considering driver type and traveling relationship between leading vehicle and following vehicle, a collision warning model based on inter-vehicle communication is studied. From aspects of inter-vehicle experiment system composition, component modules are analyzed in detail. For leading vehicle stationary, sudden deceleration, and normal traveling, three experimental schemes are proposed, and model warning performance is verified using the experiment system. Experiment results show that the algorithm model can effectively identify the collision, and precise warning rate is more than 90%. Conclusion of the study can provide a reference for research in relevant fields, such as vehicle adaptive cruise control.


Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.


2011 ◽  
Vol 12 (8) ◽  
pp. 645-654 ◽  
Author(s):  
Sheng Jin ◽  
Zhi-yi Huang ◽  
Peng-fei Tao ◽  
Dian-hai Wang

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.


2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.


Author(s):  
Mizanur Rahman ◽  
Mashrur Chowdhury ◽  
Kakan Dey ◽  
M. Rafiul Islam ◽  
Taufiquar Khan

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, ( a) the optimum velocity model (OVM) and ( b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.


Author(s):  
Vaughan W. Inman ◽  
Steven Jackson ◽  
Brian H. Philips

Cooperative Adaptive Cruise Control (CACC) has been proposed as a method to increase highway capacity and possibly enhance safety. Two experiments were conducted in a driving simulator to verify that drivers with CACC would effectively monitor the system’s longitudinal control and override the system in the event that greater braking authority was needed than the system was designed to provide. In the first experiment, the emergency response of drivers with the CACC was compared with that of drivers who manually controlled following distance within a string of vehicles. The CACC group experienced markedly fewer crashes and had longer mean time-to-collision. The second experiment examined whether the CACC safety benefit was the result of the CACC system’s limited automatic braking authority, an auditory alarm, or both. The results suggest that both auto-braking and an auditory alarm are necessary to achieve a crash reduction benefit, although the alarm alone may promote less severe collisions.


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