Collision Warning System Research Articles

A Spatial-State-Based Omni-Directional Collision Warning System for Intelligent Vehicles

A Spatial-State-Based Omni-Directional Collision Warning System for Intelligent Vehicles

The implementation of a collision warning system in Formula racing

Advanced Emergency Braking (AEB) is used to reduce rear-end collisions in critical traffic scenarios. The racing environment contributes critical cases. In Formula races, critical collisions occur during cornering. Drivers may not concentrate on the boundary regions at high speeds. For this reason, an AEB system may support the drivers in such cases. On the other hand, this system is not permitted to be implemented in racing cars. Therefore, an AEB system may be implemented into the environment of the track by using several Long-Range Radars (LRR) at critical points. The originality of this study is the approach in the implementation of an AEB system into the racing environment as a collision warning system. The effectiveness of the implementation of the AEB system into the racing environment depends on the reaction time of the racing drivers. A fuzzy logic controller was proposed to control Time to Collision (TTC) during cornering according to the track geometry. The controller design was established in MATLAB/Simulink interface with the simulations in the IPG/CarMaker environment. According to the simulation results, the approach in the implementation of the AEB system prevented a pile-up.

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.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

Modeling the influence of safety aid market penetration on traffic safety: Case of collision warning system for powered two-wheelers

Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU “Vision Zero” goal: “reduce road deaths to almost zero by 2050”. MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

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.

The Long and Winding Road: 25 Years of the National Advanced Driving Simulator.

The National Advanced Driving Simulator is a high-fidelity motion-base simulator owned by the National Highway Transportation Safety Administration and managed and operated by the University of Iowa. Its 25-year history has intersected with some of the most significant developments in automotive history, such as advanced driver assistance systems like stability control and collision warning systems, and highly automated vehicles. The simulator is an application of immersive virtual reality that uses multiprojection instead of head-mounted displays. A large-excursion motion system provides realistic acceleration and rotation cues to the driver. Due to its level of immersion and realism, drivers respond to events in the simulator the same way they would in their own vehicle. We document the history and technology behind this national facility.

A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics

Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers’ vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers’ visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles’ lateral stability and driving comfort, shorten reaction time, and relieve the drivers’ nervousness. By integrating the drivers’ visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments

With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers’ responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers’ demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human–machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers’ responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers’ characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers’ awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.

A VANET Collision Warning System with Cloud Aided Pliable Q-Learning and Safety Message Dissemination

Ease of self-driving technological developments revives Vehicular Adhoc Networks (VANETs) and motivates the Intelligent Transportation System (ITS) to develop novel intelligent solutions to amplify the VANET safety and efficiency. Collision warning system plays a significant role in VANET due to the avoidance of fatalities in vehicle crashes. Different kinds of collision warning systems have been designed for diverse VANET scenarios. Among them, reinforcement-based machine learning algorithms receive much attention due to the dispensable of explicit modeling about the environment. However, it is a censorious task to retrieve the Q-learning parameters from the dynamic VANET environment effectively. To handle such issue and safer the VANET driving environment, this paper proposes a cloud aided pliable Q-Learning based Collision Warning Prediction and Safety message Dissemination (QCP-SD). The proposed QCP-SD integrates two mechanisms that are pliable Q-learning based collision prediction and Safety alert Message Dissemination. Firstly, the designing of pliable Q-learning parameters based on dynamic VANET factors with Q-learning enhances collision prediction accuracy. Further, it estimates the novel metric named as Collision Risk Factor (CRF) and minimizes the driving risks due to vehicle crashes. The execution of pliable Q-learning only at RSUs minimizes the vehicle burden and reduces the design complexity. Secondly, the QCP-SD sends alerts to the vehicles in the risky region through highly efficient next-hop disseminators selected based on a multi-attribute cost value. Moreover, the performance of QCP-SD is evaluated through Network Simulator (NS-2). The efficiency is analyzed using the performance metrics that are duplicate packet, latency, packet loss, packet delivery ratio, secondary collision, throughput, and overhead.

Icarus: An Android-Based Unmanned Aerial Vehicle (UAV) Search and Rescue Eye in the Sky

Purpose–The purpose of this paper is to develop an unmanned aerialvehicle (UAV) using a quadcopter with the capability of videosurveillance,mapcoordinates,a deployableparachute with a medicinekit or a food pack as a payload, a collision warning system, remotely controlled, integrated with an android application to assist in search andrescue operations. 2273Method–Applied research for the development of the functional prototype, quantitative and descriptive statistics to summarize data by describing the relationship between variables in a sample or population (Kaur et al., 2018). The quadcopter underwent anevaluation using a survey instrument to test its acceptability using predefined variables to selectrespondents within Caloocan City and Quezon City, Philippines. Results–Demographic profiles and known issues and concerns were answered by 30 respondents. The results were summarized and distributed in Tables 1 and 2.Discussion-In terms of demographic profiles, the number of SAR operators within the specified areas is distributed equally, most are male, single, and within the age bracket of 31 andabove. In issues and concerns, the most common type of search and rescue was groundsearch and rescue. Human error isthe primary cause of most injuries inoperating units. The prototype was useful and everyone agreed, in terms of acceptability, drone technology will improve search and rescue operations. Conclusion–Theinnovative way of utilizing Android anddronetechnologyisanewsteptowardstheimprovementofSARoperations in the Philippines.Recommendations–The LiPo battery must be replaced with a higher capacityand the drone operator should undergo a training course and secure a permit from the Civil Aviation Authority of the Philippines (CAAP).Social Implication -Some people are scared of dronesdue to privacy issuesand fears that theycould be used to spy againstthem.

Arduino-based vehicle collision detection and warning system

As the number of vehicles increases day by day, accidents are also rapidly increasing. This surge in accidents poses a significant challenge for road safety authorities, necessitating innovative solutions and stricter enforcement of traffic regulations. Addressing the root causes, such as driver education and infrastructure improvements, is crucial to mitigating the escalating risks on our roads. These accidents can occur due to false estimation of nearby vehicles, disturbances in the mind of the driver, or any other reasons that prevent the driver from maintaining focus on driving. Therefore, not only is the accurate estimation of the distance to other surrounding vehicles necessary, but quick actions are also required to avoid any kind of accidents. In addition, advancements in technology, such as the integration of automated driving assistance systems, aim to enhance the precision of distance estimation and mitigate the risk of accidents caused by human errors. As the automotive industry continues to innovate, the intersection of human vigilance and technological support becomes crucial for ensuring road safety. This paper is specifically focused on preventing accidents. The system is designed to measure the distance between the driving car and the front object. If the driver fails to maintain the minimum safety distance from the front object, the system will warn the driver to slow down the speed of the car and avoid a collision. The driver will receive both visual and audio warnings to ensure that a collision does not occur. Additionally, the paper incorporates advanced sensor technologies and real-time data analysis to enhance the accuracy of distance measurements, providing a robust and reliable solution for accident prevention.

A Fused Data Based Real-Time Collision Warning System for Ferries in the Yangtze River

The risks for ferries in the Yangtze River are relatively high, as they frequently cross the main traffic flows, leading to more intersections with other upwards and downwards ships. Although some studies have developed many models to assess collision risks in the Yangtze River, collision warning studies on ferries are scant. Meanwhile, most of the current collision studies evaluate risk based on AIS data, which are incapable of providing real-time ship information as they are discrete-time series data. In this work, fused data combining radar and AIS data are applied in a real-time ship collision warning model to assess the dynamic risk for ferries in the Yangtze River. Firstly, data fusion technology is proposed to acquire refined ship trajectories from AIS and radar data. Then, a widely used geometric collision model is enhanced to assess the real-time collision risk for ferries. And lastly, to illustrate the model, a real case of a ferry crossing through the Yangtze River is studied. The real-time risk values of the ferries are calculated based on fused data inputs, and the output results indicate that the use of fused data provides more accurate and continuous real-time ship risks. Thus, the proposed approach is evidenced to support the development of smart maritime surveillance.

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.

Computer vision-based human-machine collision warning system on construction site

With the increase of mechanization in the construction industry, more and more construction machines appear on construction sites. This improves construction efficiency but leads to the high risk of human-machine collision. At present, human-machine collision risks are usually monitored and warned manually. It is difficult to identify relevant risks in time due to the lack of sufficient safety supervisors. This paper proposes an efficient human-machine collision warning system based on computer vision and deep learning. The designed system and relevant devices are installed on construction machinery. It can quickly identify workers and measure the distance between workers and machinery in the complex and dynamic environment so as to quickly warn the risk of human-machine collision based on a threshold. Power supply restriction and harsh environment such as dust and vibration are also considered to ensure the stable and effective operation of the system. The system was tested on a construction site for 6 weeks. The result shows that the system has a significant positive effect on the early warning of human-machine collision risk, thus benefiting the improvement of safety performance of workers in human-machine collaboration scenes.

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.

Using a V2V- and V2I-based collision warning system to improve vehicle interaction at unsignalized intersections

Introduction: Unsignalized intersections are critical components of the road network where traffic collisions occur frequently. Method: This study aims to design a Vehicle-to-Vehicle (V2V)- and Vehicle-to-Infrastructure (V2I)-based unsignalized intersection collision warning system (UICWS) to improve driver performance and enhance driver safety at unsignalized intersections. A multi-user driving simulator experiment was conducted with 48 participants divided into 24 pairs. The dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions was examined under different warning conditions (i.e., with vs without UICWS) and intersection field of view (IFOV) conditions (i.e., standard vs improved IFOV). Results and conclusions: The experimental results showed that the UICWS could effectively help drivers make appropriate operation decisions and reduce the number of right-angle collisions and near-collisions at unsignalized intersections. In the condition without UICWS, improved IFOV could prompt drivers to make crossing decisions in advance and adjust speed smoothly. Moreover, drivers’ crossing maneuvers changed with the relative distance between the subject and conflict vehicles and the intersection. The risks of collisions and near-collisions increased significantly when the two vehicles were at a similar distance to the intersection before they initiated any actions. Practical Applications: The findings show that the proposed UICWS can effectively reduce collisions or near-collisions at unsignalized intersections in a connected vehicle environment. On this basis, some active intervention strategies, such as specific speed guidance depending on the dynamics of the conflict vehicle, can be developed to ensure vehicles passing through unsignalised intersections safely.

Drowsiness Detection System Based on Machine Learning Using Eye State

Drowsiness is one of the major causes of driver-induced traffic accidents. The interactive systems developed to reduce road accidents by alerting drivers is called as Advanced Driver Assistance Systems (ADAS). The most important ADAS are Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In this study, an ADAS system based on eye state detection is presented to detect driver drowsiness. First, Viola-Jones algorithm approach is used to detect the face and eye areas in the proposed method. The detected eye region is classified as closed or open by making use of a machine learning method. Finally, the eye conditions are analyzed at time domain with PERcentage of eyelid CLOsure (PERCLOS) metric and drowsiness conditions are determined by Support Vector Machine (SVM), kNN and decision tree classifiers. The proposed methods tested on 7 real people and drowsiness states are detected at 99.77%, 94.35%, and 96.62% accuracy, respectively.

A Comprehensive Review on Advanced Driver Assistance System

In recent years, automotive industry is experiencing an unprecedented transformation with the rise of digital technologies. While in the past, acceleration, top speed, and mechanical design were the most essential factors for purchasing an automobile, electronics and software innovations define the characteristics of the future. One among such innovations is the Advanced Driver Assistance System (ADAS). This innovation is now considered as the major drive force of the automotive domain with the intelligent electronic and software architectures. ADAS is primarily designed with an objective to assist drivers by providing an alert/automate the manual tasks in any adverse conditions. ADAS functions will also have the capability to obtain the signals from roadways, Road Side Units (RSUs) and other vehicles. This research article attempts to provide a comprehensive review on the research developments and technologies used in design, development and implementation of advanced driver assistance systems. The sections included in the proposed study describe about the different ADAS methods such as adaptive and automated cruise control, smart navigation with collision warning and avoidance system, automated vehicle parking assistance and object detection. The primary goal of this research study is to achieve a collective knowledge of ADAS operational capabilities and limitations, as well as to suggest research requirements for future investigations.