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Incorporating Electronic Data into Accident Reconstructions
Published in Donald E. Struble, John D. Struble, Automotive Accident Reconstruction, 2020
Donald E. Struble, John D. Struble
Electronic data can be a very important resource for the accident reconstructionist and in some cases may be critical for successfully analyzing certain kinds of collisions. Consider, for example, a rear-end collision between a tractor-semitrailer and a modern passenger vehicle. If the passenger vehicle was produced after August 2006, the only rear structure crash tests required by U.S. Federal safety standards would be offset rear impacts by a moving deformable barrier (MDB)—in which the testing agency is not required to measure or report residual crush in either the vehicle or the MDB.1 This, combined with the absence of any frontal impact crash testing of truck tractors, would make a crush energy analysis problematic. One might turn, then, to a momentum-based analysis, but the mass mismatch between the vehicles (which may be as high as 20 to 1) would make this analytical approach somewhat less than reliable for the smaller vehicle. To understand this, Equations (19.10) and (19.11) tell us that, with a mass ratio of 20, about 95% of the closing velocity would get apportioned to the smaller vehicle in the form of velocity change (ΔV), whereas the truck would receive <5%. In the simplest case of zero restitution, a change in the closing velocity from 40 to 45 mph would cause the car’s ΔV to increase by 4.8 mph, whereas the truck would experience an increase of only 0.2 mph. Thus, the car’s ΔV is potentially much more variable than that of the truck.
Overview of Automotive Ergonomics and Human Factors
Published in Motoyuki Akamatsu, Handbook of Automotive Human Factors, 2019
This is an issue not only for the driver driving the automobile, but also for the drivers of surrounding automobiles. If the brake lamps frequently turn on and off when accelerating and decelerating to keep a constant distance between vehicles, the driver of the following vehicle may feel discomfort and may misunderstand that such an operation is being conducted intentionally with ill intent. Also, a normal driver would shorten the distance from the preceding vehicle if the traffic flow speeds down, but since the ACC tries to keep the distance constant, stronger deceleration is conducted at an earlier timing than a normal driver. The driver of the following vehicle may not have expected such behavior and may cause a rear-end collision. An understanding of humans’ driving behavior and studies on driver models are important for the system behavior to be acceptable to both the driver and other surrounding road users.
Reducing Workload: A Multisensory Approach
Published in Pamela Savage-Knepshield, John Martin, John Lockett, Laurel Allender, Designing Soldier Systems, 2018
Linda R. Elliott, Elizabeth S. Redden
Within the Prenav model, the tactile sense is described as highly intuitive and associated with fast reaction times. In fact, studies have found that tactile cues can be faster than other sensory channels when used for alarms or direction cues. For example, faster vehicle braking responses occurred in reaction to tactile rear-end collision warnings, compared to when visual cues were used (Scott and Gray 2008). In another context, operators fired more quickly in outdoor target practice using targets to the left, right, and center when using torso-mounted tactile direction cues than with visual cues (Gilson, Redden, and Elliott 2007). Tactile cues added to visual cues also yielded faster reaction times in simulation-based studies of operator decision-making and performance (for example, Calhoun et al. 2004, 2005, Forster et al. 2002). Van Erp and his colleagues found that adding tactile cues improved performance in visually demanding situations such as navigating a car through unfamiliar urban terrain (van Erp et al. 2004) and maintaining helicopter altitude in low visibility (van Erp et al. 2003). Similarly, Chiasson, McGrath, and Rupert (2002) reported faster navigation in air (high-altitude parachute environment), ground, and under water using tactile direction cues.
Adult occupant injury risk in rear impact and frontal impact: Effect of impact conditions and occupant-related factors
Published in Traffic Injury Prevention, 2022
Huipeng Chen, Agnes Kim, Jonathan Wood
In this study, the logistic regression analysis showed that occupant overall injury risk in rear impacts was lower than that in frontal impacts. Early studies (Thomas et al. 1982) illustrated the low fatality and serious injury rates in rear-end impacts, injuries in frontal crashes occurring at higher crash speeds than those of rear impacts, the major injury type in rear impacts was “whiplash” (Krafft et al. 2003; Jakobsson et al. 2004), and exposure to rear-end collision was associated with the highest risk of whiplash injury compared to other crash types. Therefore, the lower overall risk and higher AIS 1+ neck/spine injury risk in rear impacts shown in this study are not new findings. However, previous studies on rear impact focused on low-speed impacts and rarely focused on body regions other than neck.
Delay-compensating strategy to enhance string stability of adaptive cruise controlled vehicles
Published in Transportmetrica B: Transport Dynamics, 2018
M. Wang, S. P. Hoogendoorn, W. Daamen, B. van Arem, B. Shyrokau, R. Happee
However, there is no safety mechanism in this type of controller. A separate collision avoidance system has to be designed to avoid rear-end collision (Godbole et al. 1999). Nonlinear control methods for ACC systems have been proposed as well. ACC systems with variable time gap policies have been proposed (Wang and Rajamani 2004). Car-following models were used as nonlinear state feedback algorithms for ACC systems, such as the optimal velocity model (Hasebe, Nakayama, and Sugiyama 2003; Ngoduy 2015a) and the intelligent driver model (Kesting et al. 2008). Artificial intelligence techniques are also used to design ACC systems (Bifulco et al. 2013). Model predictive control, also called receding horizon control, is used in the design of ACC controllers (Wang et al. 2014a, 2014b; Li et al. 2014) recently. An advantage of the model predictive ACC controller is that it is flexible in dealing with constraints in state and control variables and it can avoid rear-end collision with the predecessor at safety-critical conditions even without the need of a separate collision avoidance system (Wang et al. 2014a). A model predictive full-speed range ACC (MP-FR-ACC) controller is proposed and tested in simulation (Wang et al. 2014a).
Analysis of rear-end crash potential and driver contributing factors based on car-following driving simulation
Published in Traffic Injury Prevention, 2022
Lerdmanus Bumrungsup, Kunnawee Kanitpong
Tailgating or driving too closely to a leading vehicle can increase the risk of rear-end collision. Among Thailand’s road accidents, tailgating is the primary cause of 14.8 percent of road crashes (Department of Land Transport 2018). Jetto et al. (2020) found a strong relationship between the rear-end crash potential and drivers’ anticipatory behavior in response to traffic conditions. Rear-end collisions often happen on the straight section of the road, where drivers have low anticipation. Tailgating can also be unintentional and caused by stressed or distracted driving as well. Several studies have revealed that most drivers fail to estimate the safe following distance (Ben-Yaacov et al. 2002; Davis and Swenson 2006).