Forward Collision Warning Research Articles

Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

Car detection algorithm in urban based on YOLOv5

Car detection is essential to promote the development of intelligent technologies such as autonomous driving and car forward collision warning. To deal with low detection accuracy, poor real-time performance and robustness of existing object detection algorithms, a YOLOv5-based car detection algorithm is used. The advanced structure design of Backbone, Neck and Head in YOLOv5 demonstrates powerful feature extraction and multi-scale object detection capabilities. The detection results reveal that the algorithm is able to quickly recognize cars in images in different urban scenes, and the detection accuracy can reach more than 95%, which can satisfy the application demands for real-time car detection.

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

Proposal of a Cost-Effective and Adaptive Customized Driver Inattention Detection Model Using Time Series Analysis and Computer Vision

Advanced Driver Assistance Systems, such as Forward Collision Warning and Lane Departure Warning, play a crucial role in accident prevention by alerting drivers to potential hazards. With the advent of fully autonomous driving technology that requires no driver input, there is now a greater emphasis on monitoring the state of vehicle occupants. This is particularly important because, in emergency situations where control must suddenly be transferred to an unprepared occupant, the risk of accidents increases significantly. To mitigate this risk, new monitoring technologies are being developed to analyze driver behavior and detect states of inattention or drowsiness. In response to the emerging demands of driver monitoring technology, we have developed the Customized Driver Inattention Detection Model (CDIDM). This model employs video analysis and statistical techniques to accurately and rapidly classify information on drivers’ gazes. The CDIDM framework defines the components of inattentive or drowsy driving based on the Driver Monitoring System (DMS) safety standards set by the European New Car Assessment Programme (EuroNCAP). By defining six driving behavior-related scenarios, we have improved the accuracy of driver inattention assessment. The CDIDM estimates the driver’s gaze while simultaneously analyzing data in real-time. To minimize computational resource usage, this model incorporates a series of preprocessing steps that facilitate efficient time series data analysis, utilizing techniques such as DTW Barycenter Averaging (DBA) and K-means clustering. This results in a robust driver attention monitoring model based on time series classification.

Driver Behavior in Response to Forward Collision Warnings Considering Driving Context

Forward collision warnings (FCW) are designed to warn drivers of potential collisions, but their effectiveness may vary based on driving conditions and driver responses. This study investigated driver behavior in response to real-world FCW alerts across various driving contexts with the aims of evaluating the prevalence and characteristics of FCW events, examining how often these events occur, their severity, and the contexts in which they happen. The current analysis considered driver responses to FCW events, including changes in vehicle kinematics, visual attention, and hand-on-wheel (HOW) behavior. Findings suggest that lower-severity FCWs are more common in local road contexts and associated with adjacent vehicles, while higher-severity FCWs were more frequent on highways and in the presence of lead vehicles. The study highlights the potential value of integrating additional contextual information into FCW systems to inform drivers better and reduce unnecessary alerts.

Looking Back on Forward Collision Warnings: A Review of Perception Response Times from Empirical Studies

Looking Back on Forward Collision Warnings: A Review of Perception Response Times from Empirical Studies

Enhanced Safety Measures for Accident Prevention in Mountainous Regions

In mountainous terrains, road safety is a paramount concern exacerbated by challenging conditions such as sharp turns and obscured visibility due to deep excavations. Forward Collision Warning (FCW) systems, employing ultrasonic sensors, serve as critical tools in identifying potential hazards, including vehicles, and issuing timely alerts to drivers. This paper introduces a controller-based approach integrating Arduino Nano and Atmega328 microcontrollers, with a specific emphasis on ultrasonic sensors, to address accident mitigation on mountainous roadways. Developing nations confront substantial road safety hurdles, with accidents emerging as a primary cause of fatalities. Embracing road design methodologies observed in developed nations like the Netherlands and Denmark holds promise for enhancing safety standards. Notably, buses and trucks are disproportionately vulnerable to accidents owing to deficient safety protocols, leading to significant loss of life. Deploying daytime lighting, high-mounted stop lamps, reflectors, and vivid attire has proven effective in mitigating accidents attributable to poor visibility. The proposed solution advocates for the strategic placement of ultrasonic sensors and LED lights ahead of and following sharp curves, respectively. Upon vehicle detection, the sensor activates the LED light, alerting oncoming traffic and empowering drivers to adjust their speed accordingly. This proactive strategy aims to curtail accidents and elevate road safety levels across mountainous regions. Index Terms: Forward Collision Warning (FCW), Mountainous terrains, high-mounted stop lamps, LED lights.

Are front crash prevention systems less effective at preventing rear-end crashes where trucks and motorcycles are struck?

Objective Automatic emergency braking (AEB) and forward collision warning (FCW) are effective at preventing rear-end crashes, but they may perform better in some rear-end crash scenarios than others. The goal of this study was to estimate the effects of front crash prevention systems equipped to passenger vehicles in crashes where another passenger vehicle, a medium/heavy truck, or a motorcycle is struck and compare effectiveness by struck vehicle type. Methods More than 160,000 two-vehicle rear-end crashes were identified where a passenger vehicle with or without FCW and AEB was the striking vehicle and another passenger vehicle, medium/heavy truck, or motorcycle was the struck vehicle. Poisson regression was used to estimate the effect of front crash prevention by struck vehicle type on rear-end crash rates per registered vehicle year, accounting for the state and year of the crash and the make, model year, class, and engine type of the striking vehicle. Results Front crash prevention was associated with a 53% reduction in rear-end crash rates when striking another passenger vehicle, which was significantly larger than the reductions of 38% when striking a medium/heavy truck and 41% when striking a motorcycle. Reductions in rear-end injury crash rates when striking a passenger vehicle also were larger than when striking a medium/heavy truck and when striking a motorcycle. Discussion If all passenger vehicles were equipped with FCW and AEB that were as effective in crashes striking a truck or motorcycle as they are in crashes with another passenger vehicle, over 5,500 additional crashes with medium/heavy trucks and 500 with motorcycles could potentially be prevented annually in the United States above what would be expected from current front crash prevention systems. Extending front crash prevention testing in consumer information programs to include motorcycle and truck targets could encourage auto manufacturers to improve performance in these crash scenarios.

Advanced Driver-Assistance Systems: Features Journey for Tomorrow

Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary advances for future ICVs. It is a moving undertaking to survey every connected work and foresee their future viewpoints, particularly for such a perplexing and interdisciplinary area of examination. Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, various type of features for ex. Lane Departure Warning (LDW), Blind Spot Monitoring (BSM), Forward Collision Warning (FCW), Automatic Emergency Braking (AEB), Traffic Sign Recognition, High Beam Assist, Rear Cross Traffic Alert, Driver Drowsiness Detection, Obstacle Aware Acceleration, Auto-steer etc. The paper aims at giving a complete picture focusing on the ADAS features for the user-friendly design of human-machine interfaces between driver and assistance system.

Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.

Effects of Advanced Driver Assistance Systems on Impact Velocity and Injury Severity: An Exploration of Data from the Crash Investigation Sampling System

This paper presents an exploratory analysis of advanced driver assistance systems (ADAS) features and safety outcomes using National Highway Transportation Safety Administration’s Crash Investigation Sampling System (CISS) database. In this database, we found improved safety outcomes associated with the presence of three ADAS features: lane departure warning, forward collision warning, and blind spot detection. In particular, vehicles with ADAS features were less likely to be involved in fatal and severe crashes than vehicles of a similar age and body style. Additionally, vehicles with ADAS features had a lower rate of change in velocity pre- and postcrash, a key proxy for injury severity. Vehicles with ADAS were also less likely to be involved in head-on and rear-end crashes than similar vehicles without ADAS and were less likely to experience a severe outcome than vehicles without ADAS when involved in sideswipe collisions. Despite these promising findings, however, small sample sizes precluded robust within-category and multivariate analysis. More robust data are urgently needed to disentangle the safety effects of ADAS features from those related to confounding variables such as vehicle age and body type for vehicle occupants, pedestrians, and bicyclists. We conclude the paper with recommendations for improvements to the CISS database that could support future investigations. We hope this work provides a baseline for scholars and practitioners to deepen our understanding of the effects of ADAS features on transportation safety.

Adaptive forward collision warning system for hazmat truck drivers: Considering differential driving behavior and risk levels

The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver’s response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems.

Characteristics of automatic emergency braking responses in passenger vehicles evaluated in the IIHS front crash prevention program

Researchers can estimate the potential safety benefits of front crash prevention (FCP) systems by simulating system performance in rear-end crash scenarios reported to police or captured during naturalistic driving. Data to support assumptions about FCP systems in production vehicles, particularly automatic emergency braking (AEB), are limited. This study used detailed information from the Insurance Institute for Highway Safety’s (IIHS's) FCP evaluation to characterize interventions in vehicles that performed well (superior-rated vehicles) and those that did not perform as well (basic/advanced-rated vehicles) when approaching a stationary surrogate vehicle on a test track at 20 and 40 km/h, and estimated performance in similar conditions at higher speeds.Vehicle and video data from 3,231 IIHS FCP tests conducted at 20 and 40 km/h and 51 IIHS FCP research tests conducted at 50, 60, and 70 km/h with AEB responses were analyzed. Forward collision warning (FCW) and AEB time-to-collision (TTC), mean deceleration, maximum deceleration, and maximum jerk from the beginning of automatic braking to the end of braking or impact were computed for each test. Each dependent measure was modeled with test speed (20 km/h, 40 km/h), IIHS FCP test rating (superior, basic/advanced), and the interaction between test speed and rating. The models were used to estimate each dependent measure at 50, 60, and 70 km/h, and model predictions were compared with the observed performance of six vehicles in IIHS research test data.Vehicles with superior-rated systems warned and began braking earlier, had a greater average rate of deceleration, reached a higher peak deceleration, and had greater jerk than vehicles with basic/advanced-rated systems, on average. The interaction between test speed and vehicle rating was significant in each linear mixed-effects model, indicating that these differences changed with test speed. FCW and AEB in superior-rated vehicles occurred 0.05 and 0.10 s earlier, respectively, per 10-km/h increase in test speed compared with basic/advanced-rated vehicles. Mean deceleration and maximum deceleration for FCP systems in superior-rated vehicles increased 0.65 m/s2 and 0.60 m/s2 more, respectively, per 10-km/h increase in test speed than for systems in basic/advanced-rated vehicles. Maximum jerk increased 2.78 m/s3 per 10-km/h increase in test speed for basic/advanced-rated vehicles but decreased 0.25 m/s3 for systems in superior-rated vehicles. The root mean square error between the observed performance and estimated values at 50, 60, and 70 km/h indicated that the linear mixed-effects model had reasonable prediction accuracy for every measure except jerk at these out-of-sample data points.The findings from this study provide insight into the characteristics that make FCP effective for preventing crashes. Based on performance in the IIHS FCP test, vehicles with superior-rated FCP systems had earlier TTC thresholds and braked with greater deceleration that increased with speed compared with basic/advanced-rated systems. The linear mixed-effects models that were developed can guide assumptions about AEB response characteristics for superior-rated FCP systems in future simulation studies.

Effects of forward collision warning and automatic emergency braking on rear-end crashes involving pickup trucks

Objective Forward collision warning (FCW) and automatic emergency braking (AEB) have found to be effective on cars, SUVs, and large trucks. The objective of this study was to extend prior work and estimate the effects of FCW and AEB on pickups. Methods Rear-end crashes where a pickup with optional FCW or AEB was the striking vehicle were identified in 25 U.S. states during 2017–2020. Quasi-induced exposure was performed with logistic regression to compare rear-end-striking crashes between vehicles with and without the systems relative to being struck in the rear. Rear-end-striking crash rates per registered vehicle year were also compared between equipped and non-equipped pickups with Poisson regression. The association of the systems with severity in the rear-end-striking crashes that occurred was examined with logistic regression. Results In the quasi-induced exposure analysis, AEB was associated with statistically significant 34% reductions in the risk of a rear-end-striking crash of any severity and with any injuries, and a 76% reduction in the risk of a rear-end-striking crash with serious or fatal injuries (p = 0.09). FCW was associated with statistically significant declines of 22% in the risk of a rear-end-striking crash with any injuries and 71% in the risk of a rear-end-striking crash with serious or fatal injuries, but FCW was not associated with a change in all rear-end-striking crashes. Results were similar in Poisson regression models for all but FCW’s effect on all rear-end-striking crashes. The odds that rear-end-striking crashes involving pickups with FCW resulted in any injuries were 26% lower, and serious/fatal injuries were 75% lower, than those involving pickups without the system. AEB was associated with a nonsignificant reduction in the odds of serious/fatal injuries when a rear-end strike occurred and no change in the odds of any injuries. Discussion Consistent benefits for front crash prevention systems have been demonstrated for a wide range of vehicle types, now including a sample of pickups. Gaps in proposed U.S. regulations should be filled so that AEB is required equipment on all new vehicles.

Artificial intelligence system for driver distraction by stacked deep learning classification

Increasing efforts in the transportation system have recently improved driver safety and reduced crash rates. Lack of attention and fatigue directly affect the driver's consciousness. Driver distraction is an essential driver-specific factor in the practical applicability of forward collision warning (FCW). However, there are still too many false alarms generated by the existing FCW system to be mitigated. This paper proposes facial detection to identify features and test anomalies' prediction against drivers using stacked convolutional neural network (CNN) layers. The proposed model used overlapping HAAR and stacked CNN features to identify classifications of eye areas, such as open or closed. In addition to the sliding query window's overall intensity information. The conventional HAAR function, which elevates the brightness of nearby regions, is still preferable. This method considers current intelligent transportation system-based solutions to minimize distraction effects by continuously comparing with flexible thresholds. The experimental results are analyzed from accurate driving datasets. At 456 iterations, the results acquired over 80% accuracy, while loss is near zero. The implication of driver's risk tolerance is further explored in this manner. Several risks are connected to driving any type of transportation system.

On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% – lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.

Drivers’ reactions to connected vehicle forward collision warnings: Leveraging real-world data from the THEA CV pilot

The Tampa Hillsborough Expressway Authority Connected Vehicle Pilot Deployment (THEA CV Pilot) implemented several vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) applications on more than 1,000 private vehicles. This paper focuses on the Forward Collision Warning (FCW) application to study factors that are associated with drivers’ reactions to FCWs and to investigate if the observed driving styles derived from the data support the participants’ stated driving styles obtained from their survey responses. A panel of participants, driving in real-world traffic conditions for over two years with retrofitted CV technology and integrated FCW application, is used. The panel consists of a treatment (Human Machine Interface (HMI) enabled) and a control (HMI disabled) group. Random parameters logit and correlated grouped random parameter logit models are estimated to reveal possible associations between stated and observed driving behavior, HMI exposure, socio-demographic factors, and the response variable (drivers’ reaction to FCW). The study found an association between one measure of driving volatility, so that with increased driving volatility (proxy for driving aggressiveness), the probability of reaction to FCW declines. The study also found that the probability of reaction for drivers who received a warning (audiovisual) via HMI increased by 9.93 % compared to those who did not receive a warning.

Forward collision warning system using multi-modal trajectory prediction of the intelligent vehicle

As one of important advanced driving assistance systems (ADAS) for intelligent vehicles, forward collision warning system is an effective solution to avoid the traffic accidents. Accurately trajectory prediction can obtain the future spatial distribution of the vehicles in contextual traffic, which will help the driver make the decisions and avoid potential collisions. This paper proposes an effective trajectory prediction method based on long short term memory model integrated with attention mechanism and regularization strategy (AR-LSTM). The long short-term memory and convolutional neural network (LSTM-CNN) is utilized to recognize the driver’s lane change intention. Combined with the multi-modal information of the vehicles such as vehicle status variables, traffic information, and driver’s lane change intention, the AR-LSTM model is designed to predict the vehicle’s future trajectory. Besides, the AR-LSTM and LSTM- CNN model are trained and tested by the real traffic data set NGSIM (next generation simulation). Finally, considering two maneuvers to avoid the collisions by braking or changing lane, the proposed forward collision warning system is verified by the hardware-in-loop (HIL) platform compared to the existed fixed TTC (time to collision) method. And the statistical results demonstrate that the proposed system can provide correct warnings of braking and decrease invalid warnings of braking.

An Adaptive Multi-Staged Forward Collision Warning System Using a Light Gradient Boosting Machine

The existing forward collision warning (FCW) systems that adopt kinematic or perceptual parameters have some drawbacks in the warning performance because of poor adaptability to the users or ineffectiveness of the warnings. To solve the problems of adaptability, several FCW models have been proposed based on algorithms (machine learning, deep learning). However, there is a lack of consideration for the multi-staged warning to avoid an abrupt warning that may startle or distract the driver. In this study, a light gradient boosting machine (LGBM) was adopted to develop a multi-staged FCW. The proposed model was trained and evaluated on a platform based on a driving simulator by twenty drivers. Through Shapley Additive Explanations (SHAPs), the output of the proposed model was explained. Specifically, the front vehicle acceleration, time-to-collision (TTC), and relative speed were found to strongly affect the warning stages from the proposed model. To evaluate the utility and acceptability of the developed model, it was compared with three existing FCW models in terms of subjective and objective indicators. As a result, a trade-off was found between the utility and user acceptance. Additionally, the comparison study also indicated that the developed model outperformed other previous models due to not only the high accuracy but also the suitable trigger timing for each participant.

An In-Vehicle Warning Information Provision Strategy for V2V-Based Proactive Traffic Safety Management

The availability of vehicle interaction data, which is obtained by an in-vehicle forward collision warning system, including spacing between the leading and the following vehicle and time-to-collision, provides a valuable opportunity to predict crash risks in real time. When this opportunity is combined with connected vehicle technologies including vehicle-to-vehicle wireless communications, it is expected that more effective crash prevention would be achievable by providing predictive warning information as a part of proactive traffic safety management (PTSM). The purpose of this study is to develop a more reliable in-vehicle warning information provision strategy based on the prediction of crash risks using vehicle interaction data. A crash risk prediction model based on a long short-term memory was able to predict the crash risk after 3 seconds with a mean absolute percentage error of 8% using the data for the past 5 seconds. The predicted crash risk data were applied to derive the optimal threshold for triggering in-vehicle warning information, which is the essence of the proposed warning provision strategy. This study defined three indicators to evaluate the reliability of warning information: correct detection rate (CDR), detection failure rate (DFR), and information provision rate (IPR). An exemplar analysis result showed that the optimal threshold to minimize IPR in a situation where CDR and DFR are 100% and 0%, respectively, was identified as 0.69. The proposed methodology that predicts crash risks in real time and provides V2V-based warning information in a more proactive manner is expected to mitigate the crash risk significantly.