Forward Collision Warning System Research Articles

Comparison of Experienced and Novice Drivers' Visual and Driving Behaviors during Warned or Unwarned Near-Forward Collisions.

Forward collision warning systems (FCWSs) monitor the road ahead and warn drivers when the time to collision reaches a certain threshold. Using a driving simulator, this study compared the effects of FCWSs between novice drivers (unlicensed drivers) and experienced drivers (holding a driving license for at least four years) on near-collision events, as well as visual and driving behaviors. The experimental drives lasted about six hours spread over six consecutive weeks. Visual behaviors (e.g., mean number of fixations) and driving behaviors (e.g., braking reaction times) were collected during unprovoked near-collision events occurring during a car-following task, with (FCWS group) or without FCWS (No Automation group). FCWS presence reduced the number of near-collision events drastically and enhanced visual behaviors during those events. Unexpectedly, brake reaction times were observed to be significantly longer with FCWS, suggesting a cognitive cost associated with the warning process. Still, the FCWS showed a slight safety benefit for novice drivers attributed to the assistance provided for the situation analysis. Outside the warning events, FCWS presence also impacted car-following behaviors. Drivers took an extra safety margin, possibly to prevent incidental triggering of warnings. The data enlighten the nature of the cognitive processes associated with FCWSs. Altogether, the findings support the general efficiency of FCWSs observed through a massive reduction in the number of near-collision events and point toward the need for further investigations.

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.

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.

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.

Improving the safety relevance of automatic emergency braking testing programs: An examination of common characteristics of police-reported rear-end crashes in the United States

Objective Forward collision warning and automatic emergency braking (AEB) systems help prevent rear-end crashes where a vehicle strikes the rear of another. However, the benefits may be limited if the systems are stymied by common crash circumstances or only target scenarios evaluated in vehicle testing programs. This study examined the prevalence of characteristics that may limit AEB system performance in police-reported rear-end crashes and the relevance of scenarios used to evaluate these systems. Methods Police-reported rear-end crashes (n = 6,731,215, Crash Report Sampling System) and fatal rear-end crashes (n = 4,285, Fatality Analysis Reporting System) with a fatality in the striking or struck vehicle during 2016–2019 were analyzed. Percentages of police-reported rear-end crashes, nonfatal-injury rear-end crashes, and fatal rear-end crashes were computed to identify common crash characteristics. Roadway speed limit was used as a proxy for striking vehicle speed. Results A straight-moving vehicle striking a stopped or decelerating vehicle on roads with a speed limit of 40 km/h (25 mph) or less only accounted for 3% of all rear-end crashes, 3% of nonfatal-injury rear-end crashes, and 1% of fatal rear-end crashes. In contrast, 36% of all rear-end crashes, 36% of nonfatal-injury rear-end crashes, and 11% of fatal rear-end crashes involved a straight-moving vehicle striking a stopped or decelerating vehicle on roads with a speed limit between 56 and 72 km/h (35 and 45 mph). A medium or heavy truck was the struck vehicle in 32% of fatal rear-end crashes, and a motorcycle was the struck vehicle in 11% of fatal rear-end crashes. At least one of the following characteristics that may degrade AEB system performance was present in 14% of the rear-end crashes studied: striking vehicle turning; a struck vehicle turning or changing lanes; a struck vehicle that is not a passenger vehicle; wintery weather; wet or icy roads; or a speed limit of 113 km/h (70 mph) or higher. Conclusion Circumstances shown to diminish AEB effectiveness accounted for 14% of rear-end crashes, and scenarios currently used to evaluate AEB systems accounted for 3%. Evaluating AEB systems at speeds up to 72 km/h (45 mph) and incorporating a motorcycle or medium/heavy truck target will make AEB evaluations more representative of police-reported rear-end crashes.

Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems.

Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle’s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is.

Design of a Basic Vehicular Communication Network using Radar and Camera Sensor with Enhanced Safety Features

The primary goal of Vehicular Ad-hoc Network (VANET) technology’s development is to increase road safety and enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication in order to share crucial information and prevent road accidents. VANET establishes a mobile network between moving cars by considering each vehicle as a separate entity. Safety applications in VANET are currently receiving a lot of attention from researchers as well as automobile manufacturers. This paper concentrate on simulation-based safety-critical techniques in vehicle networks employing radar and video sensors in various road styles. This paper discusses about how to create a simulation-based driving scenario environment and calculates the fluctuations in sensor detection rates depending on different driving conditions. And also created a basic Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication network. An integrated approach is also proposed to Forward Collision Warning System (FCWS) in different cases.

Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems.

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.

Hybrid Interacting Multiple Model Filtering for Improving the Reliability of Radar-Based Forward Collision Warning Systems.

Automotive forward collision warning (FCW) systems based on radar sensors attracted widespread attention in recent years. To achieve a reliable FCW, it is essential to accurately estimate the position and velocity of a preceding vehicle. To this end, this study proposed a novel estimation algorithm, which is a hybrid of interacting multiple model probabilistic data association (IMM-PDA) and finite impulse response (FIR) filters. Although the IMM-PDA filter is one of the most successful algorithm for tracking a maneuvering target in clutters, it sometimes exhibits divergence owing to modeling errors. In this study, the divergent IMM-PDA filter in the novel algorithm was reset and recovered using an assisting FIR filter. Consequently, this enabled reliable estimation for FCW. The improved reliability of the proposed algorithm was demonstrated through the simulation of preceding vehicle tracking using automotive radars.

Evaluating the Associations between Forward Collision Warning Severity and Driving Context

Forward collision warning (FCW) systems typically employ forward sensing technologies to identify possible forward collisions and provide an alert to the driver in the event they have not recognized a threat. These systems have demonstrated safety benefits. However, because the base rate of collisions is low, sensitive FCW systems can provide a high rate of alarms in situations with no or low probability of collision, which may negatively impact driver responsiveness and satisfaction. We examined over 2000 naturally occurring FCWs in two modern vehicles as a part of a naturalistic driving study investigation into advanced vehicle technologies. Analysts used cabin and forward camera footage, as well as environmental characteristics, to judge the likelihood of a crash during each alert, which were used to model the likelihood of an alert representing a possible collision. Only nine FCWs were considered “crash possible and imminent”. Road-type, speed, traffic density, and deceleration profiles distinguished between alert severity. Modeling outcomes provide clues for reducing nuisance and false alerts, and the method of using subjective video annotation combined with vehicle kinematics shows promise for investigating FCW alerts in the real world.

Integrated System Design and Safety Framework for Model-Based Safety Assessment

The increased complexity of modern engineered systems has introduced novel challenges for assessing their safety early in the life cycle. For example, due to the iterative nature of the design and safety life cycle, there is constant data transformation and feedback of information between the system design models, safety analyses, and safety verification. Data transformation and feedback are often manually performed by engineers, which is time-consuming and error prone and can introduce inconsistencies in safety assessments. Although several model-based systems engineering approaches have been developed for safety analysis and safety verification, current approaches do not address the inconsistencies introduced in the safety assessment process. This study describes the Integrated System Design and Safety (ISDS) framework, which is a model-based safety assessment framework that aims to eliminate such inconsistencies. The framework combines a model-based safety analysis approach with a model-based safety verification. This paper extends previous work, which focused on the model-based safety analysis approach, to describe the model-based safety verification approach adopted in the ISDS framework. Safety verification is performed using a simulation-based fault injection approach and enabled by a fault injection engine, which injects failures into the system design and characterizes system behaviors to identify safety violations impacting the system. The results from the case study, in which the framework is used to assess the safety of a forward collision warning system, highlight that the algorithms and automated feedback loops of the framework can reduce inconsistencies in the safety assessment process while also identifying safety violations impacting the system.

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead.The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality.The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.

Modeling commercial vehicle drivers’ acceptance of advanced driving assistance system (ADAS)

PurposeAdvanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approachA random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.FindingsThe acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/valueFew research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

A Forward-Collision Warning System for Electric Vehicles: Experimental Validation in Virtual and Real Environment

Driver behaviour and distraction have been identified as the main causes of rear end collisions. However a promptly issued warning can reduce the severity of crashes, if not prevent them completely. This paper proposes a Forward Collision Warning System (FCW) based on information coming from a low cost forward monocular camera for low end electric vehicles. The system resorts to a Convolutional Neural Network (CNN) and does not require the reconstruction of a complete 3D model of the surrounding environment. Moreover a closed-loop simulation platform is proposed, which enables the fast development and testing of the FCW and other Advanced Driver Assistance Systems (ADAS). The system is then deployed on embedded hardware and experimentally validated on a test track.

Effect Evaluation of Forward Collision Warning System Using IoT Log and Virtual Driving Simulation Data

Advanced driver-assistance systems (ADAS) are primarily known for their positive impact in improving the safety of drivers. Previous studies primarily analyzed the positive effects of ADAS with short-term experiments and accident data without considering the long-term changes in drivers’ safety perception. The human factor is the most dominant among factors that cause traffic accidents, and safety effect evaluation should be performed considering changes in human errors. To this end, this study classified the safety effect of ADAS-forward collision warning (FCW) on taxi drivers in Seoul into behavioral control and attitude change to perform analysis on respective factors. With regard to behavioral control, virtual driving simulation was used to analyze the reaction time of drivers and deceleration rate, and for attitude change, autoregressive integrated moving average (ARIMA) time series analysis was employed to predict the long-term perception change of drivers. The analysis results indicated that, in terms of behavioral control, ADAS-FCW reduces the cognitive reaction time of drivers in risk situations on the road, similar to the findings in previous studies. However, in terms of attitude change, ADAS-FCW has the adverse long-term effect of increasing violations in maintaining safety distance in the case of nighttime-drivers under 60 years old. As can be seen from these results, new technologies in the road safety arena can have a short-term effect of improving safety with behavioral control but may have a negative impact in the long term. The results of this study are expected to provide a theoretical basis for reference in the safety evaluation of ADAS and traffic safety facilities.

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.

UAV Collision Detection Algorithm based on Formulafor Fast Calculation

Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.