Crash avoidance and driver assistance technologies – Are they used?

Reagan, Ian J.; Cicchino, Jessica B.; Kerfoot, Laura; Weast, Rebecca A.

doi:10.1016/j.trf.2017.11.015

Cited by 77 publications

(23 citation statements)

References 22 publications

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“…The escalation procedure should use an additive process whereby more alert modalities are included and message urgency increases with each progressive phase; for example, audible alert volume increases or timbre changes, visual icon color changes from green to yellow to red, text-based instructions change in phrasing to become more directive, and tactile alert vibration increases in frequency or amplitude (Campbell et al, 2018; Cao et al, 2010; Politis et al, 2013). Escalating attention reminders should employ urgency mapping strategies because these alerts must balance the need to bring the driver back into the loop in a timely manner while avoiding annoying, distracting, or overwhelming the driver, which could lead to drivers disusing the system (Reagan et al, 2018). Calibrating the urgency of alerts to be appropriate for the driving situation and driver state should help to facilitate quicker driver adherence to the attention reminders while reducing perceived annoyance (Marshall et al, 2007).…”

Section: Driver Management Strategiesmentioning

confidence: 99%

Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design

Mueller

Reagan

Cicchino

2021

Journal of Cognitive Engineering and Decision Making

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Level 2 driving automation has the potential to reduce crashes; however, there are known risks when using these systems, particularly as they relate to drivers becoming disengaged from driving. This paper provides data-driven recommendations for Level 2 driving automation design using the best currently available methods to encourage driver engagement and communicate where and how a system can safely be used. Our recommendations pertaining to driver engagement concern driver management systems that monitor the driver for signs of disengagement and return the driver to the loop using a multimodal escalation process with attention reminders, countermeasures for sustained noncompliance to the attention reminders, and proactive methods for keeping drivers engaged with respect to driver-system interactions and system functionality considerations. We also provide guidance on how the operational design domain (ODD), driver responsibilities, and system limitations should be communicated and how these systems must be self-limited within the ODD. In addition, we discuss the benefits and limitations of training to emphasize the importance of making these systems intuitive to all users, regardless of training, to ensure proper use. These recommendations should be applied as a whole, because selectively adhering to only some may inadvertently exacerbate the dangers of driver disengagement.

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Section: Driver Management Strategiesmentioning

confidence: 99%

Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design

Mueller

Reagan

Cicchino

2021

Journal of Cognitive Engineering and Decision Making

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“…However, modern studies have been focusing on making EVs comfortable with autonomous features. Some autonomous features such as self-parking and crash avoidance have already been in practice [5,6]. The development and research on EVs have not stopped, but the focus is significantly shifted towards autonomy in vehicles.…”

Section: Introductionmentioning

confidence: 99%

Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles

Ahmad

Malik

Park

et al. 2019

Sensors

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Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.

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“…AEB may not be designed to consistently activate in some nonstandard situations because of concerns that addressing these scenarios with current sensing technology would result in unnecessary activations, which could lead drivers to disuse the system because they are annoyed by it or have lost trust in it (Kidd and Reagan 2019;Lee and See 2004;Parasuraman and Riley 1997). Consumer use of AEB currently is high, but lane departure warning has suffered from considerable disuse due in part to user annoyance, which limits the technology's effectiveness (Braitman et al 2010;McCartt 2014, 2016;Flannagan et al 2016;Reagan et al 2018;Reagan and McCartt 2016). Work to improve the performance of AEB in atypical scenarios will need to ensure that such designs do not adversely affect AEB use resulting from potential unwanted activations, that performance is not compromised in more typical situations, and that AEB activation while the driver is actively maneuvering the vehicle (i.e., turning) does not create unanticipated safety consequences.…”

Section: Discussionmentioning

confidence: 99%

“…Use of systems by drivers is essential, because technologies cannot work if drivers turn them off. Based on current front crash prevention designs examined in the literature, most owners of vehicles with forward collision warning and AEB have reported in surveys that they always keep the systems activated (Braitman et al 2010;Cicchino and McCartt 2015;McCartt 2014, 2016;McDonald et al 2018), and observational studies of vehicles with front crash prevention brought to dealerships for service have found that nearly all vehicles observed had their systems turned on (Reagan et al 2018;Reagan and McCartt 2016). AEB does not rely on a response from drivers to activate, but emergency braking may mitigate the severity of a crash rather than prevent a crash altogether.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking

Cicchino

Zuby

2019

Traffic Injury Prevention

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Objectives: Automatic emergency braking (AEB) is a proven effective countermeasure for preventing front-to-rear crashes, but it has not yet fully lived up to its estimated potential. This study identified the types of rear-end crashes in which striking vehicles with AEB are overrepresented to determine whether the system is more effective in some situations than in others, so that additional opportunities for increasing AEB effectiveness might be explored. Methods: Rear-end crash involvements were extracted from 23 U.S. states during 2009-2016 for striking passenger vehicles with and without AEB among models where the system was optional. Logistic regression was used to examine the odds that rear-end crashes with various characteristics involved a striking vehicle with AEB, controlling for driver and vehicle features. Results: Striking vehicles were significantly more likely to have AEB in crashes where the striking vehicle was turning relative to when it was moving straight (odds ratio [OR] ¼ 2.35; 95% confidence interval [CI], 1.76, 3.13); when the struck vehicle was turning (OR ¼ 1.66; 95% CI, 1.25, 2.21) or changing lanes (OR ¼ 2.05; 95% CI, 1.13, 3.72) relative to when it was slowing or stopped; when the struck vehicle was not a passenger vehicle or was a special use vehicle relative to a car (OR ¼ 1.61; 95% CI, 1.01, 2.55); on snowy or icy roads relative to dry roads (OR ¼ 1.83; 95% CI, 1.16, 2.86); or on roads with speed limits of 70þ mph relative to those with 40 to 45 mph speed limits (OR ¼ 1.49; 95% CI, 1.10, 2.03). Overall, 25.3% of crashes where the striking vehicle had AEB had at least one of these overrepresented characteristics, compared with 15.9% of strikes by vehicles without AEB. Conclusions: The typical rear-end crash occurs when 2 passenger vehicles are proceeding in line, on a dry road, and at lower speeds. Because atypical crash circumstances are overrepresented among rear-end crashes by striking vehicles with AEB, it appears that the system is doing a better job of preventing the more typical crash scenario. Consumer information testing programs of AEB use a test configuration that models the typical rear-end crash type. Testing programs promoting good AEB performance in crash circumstances where vehicles with AEB are overrepresented could guide future development of AEB systems that perform well in these additional rear-end collision scenarios. ARTICLE HISTORY

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Crash avoidance and driver assistance technologies – Are they used?

Cited by 77 publications

References 22 publications

Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design

Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design

Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles

Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking

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