Collision Warning Research Articles

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.

Driving Performance Evaluation of Shuttle Buses: A Case Study of Hong Kong-Zhuhai-Macau Bridge.

The risky behaviours of bus drivers are of great concern to public health and environmental sustainability, especially for the buses operated between cities. With this in mind, the present study examined the distribution of risky behaviours among bus drivers, and the contributing factors to risky performance. To achieve this, 1648 records of GPS trajectory data and 8281 records of advance warning message data from Hong Kong–Zhuhai–Macau Bridge shuttle buses were obtained. The temporal and spatial distribution of risky behaviours was analysed. A random parameters negative binomial model was developed to further investigate the relationship between speed-related factors and risky behaviours. The results indicated that the warning of safety distance, lane departure, forward collision, and distraction were more likely to occur on weekdays. The period between 14 and 16 o’clock obtained the highest frequency of safety distance and lane departure warnings. Regarding the model estimation results, indicators reflecting average speed, acceleration, and number of trips per day showed a statistically significant impact on safety distance and lane departure warnings. Also, the acceleration of bus drivers showed a mixed impact on lane departure warnings. Corresponding implications were discussed according to the findings to reduce the frequency of risky behaviours in shuttle bus operations.

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.

How Does C-V2X Help Autonomous Driving to Avoid Accidents?

Accidents are continuously reported for autonomous driving vehicles including those with advanced sensors installed. Some of accidents are usually caused by bad weather, poor lighting conditions and non-line-of-sight obstacles. Cellular Vehicle-to-Everything (C-V2X) radio technology can significantly improve those weak spots for autonomous driving. This paper describes one of the C-V2X system solutions: Vulnerable Road User Collision Warning (VRUCW) for autonomous driving. The paper provides the system architecture, design logic, network topology, message flow, artificial intelligence (AI) and network security feature. As a reference it also includes a commercial project with its test results.

Instantaneous Attitude Estimation of Spacecraft Utilizing Joint Optical-and-ISAR Observation

Estimation of the instantaneous attitude of spacecraft plays significant roles in space situation awareness activities, such as on-orbit status monitoring and collision warning. With the rapid development of both optical sensors and inverse synthetic aperture radar (ISAR), it becomes possible to achieve accurate instantaneous attitude estimation of spacecraft by multistatic optical-and-ISAR joint observation. In view of this, this article proposes a novel spacecraft attitude estimation method based on a joint optical-and-ISAR observation system, which includes one optical sensor and two ISARs. Specifically, the proposed method first estimates the orientation and the length of typical components utilizing optical and ISAR images with the same observation instant. Then, it solves the target instantaneous rotation vector from the orientation, length, and Doppler of typical components, and finally, it obtains the instantaneous attitude of the spacecraft. Experimental results have verified the effectiveness of the proposed method.

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.

A vision-based human-robot collaborative system for digital twin

Flexible and safe human-robot collaboration depends on accurately capturing the three-dimensional motion of humans and robots in the field of smart manufacturing. In this paper, a novel approach to developing a human-robot collaborative assembly system is proposed and applied to the field of digital twins. Within the context, a deep learning-based model is explored to develop a depth camera-based human recognition system for accurate prediction of key points for human skeletons model and high-precision human localisation in a human-robot collaborative setting. After the functional mapping of robot calibration, a collision warning module leverages coordinates of key human-robot points to facilitate efficient and safe human-robot collaborative assembly.

Developing a New Omni-Directional Collision Warning System for Intelligent Vehicles

Developing a New Omni-Directional Collision Warning System for Intelligent Vehicles

Vehicles, Advanced Features, Driver Behavior, and Safety: A Systematic Review of the Literature

Driver errors contribute to more than 94% of traffic crashes. Automotive companies are striving to enhance their vehicles to eliminate driver errors and reduce the number of crashes. Various advanced features like lane departure warning (LDW), blind spot warning (BSW), over speed warning (OSW), forward collision warning (FCW), lane keep assist (LKA), adaptive cruise control (ACC), cooperative ACC (CACC), and automated emergency braking (AEB) are designed to assist with, or in some cases take over, certain driving maneuvers. They can be broadly categorized into advanced driver assistance system (ADAS) and automated features. Each of these advanced features focuses on addressing a particular task of driving, thereby, aiding the driver, influencing their behavior, and enhancing safety. Many vehicles with these advanced features are penetrating into the market, yet the total reported number of crashes has increased in recent years. This paper presents a systematic review of these advanced features on driver behavior and safety. The review is categorized into 1) survey and mathematical methods to assess driver behavior, 2) field test methods to assess driver behavior, 3) microsimulation methods to assess driver behavior, 4) driving simulator methods to assess driver behavior, and 5) driver understanding and the effectiveness of advanced features. It is followed by conclusions, knowledge gaps, and need for further research.

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.

Vulnerable road user safety evaluation using probe vehicle data with collision warning information

Recently, connected vehicle (CV) and advanced driver assistance system (ADAS) technologies, including retrofit ADAS products, have been introduced in the real-world market. This study focuses on pedestrian collision warning (PCW) as an intensive function of the ADAS, which operates when a vehicle is at a collision risk with a vulnerable road user (VRU). Although several studies have been conducted on surrogate safety measures for crashes against VRUs, none of these studies used real-world CV data with collision warning information. Thus, the current study aims to i) develop a safety performance function (SPF) for crashes against VRUs at unsignalized intersections, where the PCW information was acquired using connected advanced probe vehicles (APVs), and ii) assess the effectiveness of a traffic-safety treatment implemented at an unsignalized intersection based on the developed SPF. In particular, this study proposes a two-step empirical Bayesian estimation based on the SPF model (2-step EB-SPF) to consider the issue regarding the limited number and vehicle types of APVs that can obtain PCW information. Based on the APV data, the vehicle–VRU crash-count negative binomial (NB) models were separately estimated using the actual PCW incidence rate and the EB estimate of PCW incidence rate, respectively. Although the actual PCW incidence rate was not statistically significant in the former model, the EB estimate of the PCW incidence rate was statistically significant and positively related to the crash count in the latter model. Moreover, a traffic-safety treatment was implemented at an unsignalized intersection and subsequently assessed as a case study based on the estimated 2-step EB-SPF model. Consequently, the model with the EB estimate of PCW incidence rate revealed that the vehicle–VRU crash risk was reduced by approximately 70%, and it was statistically significant at the 99% confidence level, which diminished the confidence interval in comparison to the model without the PCW incidence rate. Thus, the APV data including collision warning information can improve the estimation accuracy of determining the effect of the traffic-safety treatment, which can considerably contribute toward traffic safety assessment, especially for short after-treatment periods such as that prevailing in this case study.

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.

5G-V2X Based Traffic Safety Warning System through Mobile Sensor and Wireless Communication

This project designs a traffic safety early warning system based on 5G-V2X for the current situation of increasing traffic accidents in China, which concentrates on two modules of V2V (vehicle-to-vehicle), V2I (vehicle-to-road) for early warning system design, with OBU (vehicle communication unit) and RSU (roadside communication unit) based on 5G-V2X communication technology to establish vehicle-to-vehicle and vehicle-to-road interactive communication, and realize V2V collision warning and V2I traffic light emergency event warning at intersections through collision risk warning algorithm and intersection passage assistance algorithm, thus alerting drivers to avoid dangerous situations and reducing the incidence of traffic accidents.

Effects of an integrated collision warning system on risk compensation behavior: An examination under naturalistic driving conditions

Collision warning systems can improve traffic safety, while their safety benefit may be lessened due to improper risk compensation or system misuse. There are limited studies of advanced safety systems increasing unexpected risky driving behavior, especially with adolescent drivers. This study is designed to address this research gap in two main areas: 1) it seeks to examine whether and how the introduction of advanced driver-assistance systems influences drivers’ risk compensation behavior (e.g., increase of hard braking frequency), and 2) it investigates key factors (e.g., distraction) that contribute to changes in hard braking frequency during driving for both teen and adult drivers. Naturalistic driving data from two previous studies were analyzed in this study with two methods: a hierarchical logistic regression model was used to evaluate the effects of an integrated collision warning system on hard braking behavior, while a Random forests algorithm was applied to model hard braking behavior and to rank the contributing factors by calculating the importance scores. No statistical evidence was observed that the integrated collision warning system significantly changed the likelihood of hard braking for teen or adult drivers. Other factors like distraction, especially visual-manual distraction, had the largest impact on the hard braking behavior, followed by speeding and roadway segments (i.e., at intersections or not). Short time-headways and driving in high-density traffic significantly increased the likelihood of hard braking. Furthermore, the rate of hard braking behavior on surface roads was much higher than on highways, as expected. Compared with straight road segments, hard braking behavior was less likely to occur on curve roads. This study applied an analytical strategy by using both machine learning and statistical analysis methods to achieve high model accuracy and facilitate inference concerning the relationships among variables. Findings in this study can help to improve the design of integrated collision warning systems and the use of autonomous braking systems, and to apply appropriate analysis methods in understanding teen drivers’ behavior changes with those safety systems.

In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study

Objective Motor vehicle crashes remain a significant problem. Advanced driver assistance systems (ADAS) have the potential to reduce crash incidence and severity, but their optimization requires a comprehensive understanding of driver-specific errors and environmental hazards in real-world crash scenarios. Therefore, the objectives of this study were to quantify contributing factors using the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS), identify potential ADAS interventions, and make suggestions to optimize ADAS for real-world crash scenarios. Methods A subset of the SHRP 2 NDS consisting of at-fault crashes (n = 369) among teens (16–19 yrs), young adults (20–24 yrs), adults (35–54 yrs) and older adults (70+ yrs) were reviewed to identify contributing factors and potential ADAS interventions. Contributing factors were classified according to National Motor Vehicle Crash Causation Survey pre-crash assessment variable elements. A single critical factor was selected among the contributing factors for each crash. Case reviews with a multidisciplinary panel of industry experts were conducted to develop suggestions for ADAS optimization. Critical factors were compared across at-risk driving groups, gender, and incident type using chi-square statistics and multinomial logistic regression. Results Driver error was the critical factor in 94% of crashes. Recognition error (56%), including internal distraction and inadequate surveillance, was the most common driver error sub-type. Teens and young adults exhibited greater decision errors compared to older adults (p < 0.01). Older adults exhibited greater performance errors (p < 0.05) compared to teens and young adults. Automatic emergency braking (AEB) had the greatest potential to mitigate crashes (48%), followed by vehicle-to-vehicle communication (38%) and driver monitoring (24%). ADAS suggestions for optimization included (1) implementing adaptive forward collision warning, AEB, high-speed warning, and curve-speed warning to account for road surface conditions (2) ensuring detection of nonstandard road objects, (3) vehicle-to-vehicle communication alerting drivers to cross-traffic, (4) vehicle-to-infrastructure communication alerting drivers to the presence of pedestrians in crosswalks, and (5) optimizing lane keeping assist for end-departures and pedal confusion. Conclusions These data provide stakeholders with a comprehensive understanding of critical factors among at-risk drivers as well as suggestions for ADAS improvements based on naturalistic data. Such data can be used to optimize ADAS for driver-specific errors and help develop more robust vehicle test procedures.

Safety effect analysis of motorcycle V2I collision warning system

Connected vehicle technology can help the driver make decisions, improve awareness of the road environment, and thereby possibly enhance traffic safety. This study explored the impact of a motorcycle vehicle-to-infrastructure (V2I) warning system on road safety through a field trial of a Motorcycle Safety Warning System (MSWS). This study used principal component analysis, K-means, and ordinal logistic regression to explore the effects of the MSWS. After system activation, the road safety level (RSL) improved at 50% of the field trial sites. This study provides a reference for evaluating RSLs through the composite index of this study and verifies that the V2I warning signs of the MSWS provide a certain degree of improvement of motorcycle traffic safety. A V2I system like the MSWS can be used to collect data on driving behaviours in a short period for safety effect analysis, as compared to crash data, which require an extended period. In addition, the traffic behaviour data collected by a V2I system can also be used to identify potential high crash risk locations. The warning signs developed in this study can prevent accidents by alerting drivers who are speeding and at risk of crashes.

Stereo Vision-Based Road Debris Detection System for Advanced Driver Assistance Systems

Reliable detection of obstacles around an autonomous vehicle is essential to avoid potential collision and ensure safe driving. However, a vast majority of existing systems are mainly focused on detecting large obstacles such as vehicles, pedestrians, and so on. Detection of small obstacles such as road debris, which pose a serious potential threat are often overlooked. In this article, a novel stereo vision-based road debris detection algorithm is proposed that detects debris on the road surfaces and estimates their height accurately. Moreover, a collision warning system that could warn the driver of an imminent crash by using 3D information of detected debris has been studied. A novel feature-based classifier that uses a combination of strong and weak features has been developed for the proposed algorithm, which identifies debris from selected candidates and calculates its height. 3D information of detected debris and vehicle’s speed are used in the collision warning system to warn the driver to safely maneuver the vehicle. The performance of the proposed algorithm has been evaluated by implementing it on a passenger vehicle. Experimental results confirm that the proposed algorithm can successfully detect debris of ≥5 cm height for up to a 22 m distance with an accuracy of 90%. Moreover, the debris detection algorithm runs at 20 Hz in a commercially available stereo camera making it suitable for real-time applications in commercial vehicles.

Evaluating connected vehicle-based weather responsive management strategies using weather-sensitive microscopic simulation

The purpose of this study is to perform analysis, modeling, and simulation (AMS) to investigate the effectiveness of connected vehicle (CV)-based Weather Responsive Management Strategies (WRMS) to address safety concerns on freeway corridors under adverse weather conditions. This study investigates three CV-based WRMS applications: Forward Collision Warning (FCW), Early Lane Change (ELC) advisory, and Variable Speed Limit (VSL), designs operational alternatives for WRMS using CV data, and develops an AMS tool using a weather-sensitive microscopic traffic simulator to understand the effectiveness of the three WRMS under different scenarios. Various CV market penetration rates (MPR), weather conditions, and WRMS algorithm settings are tested in this study. The case study is based on a real-world freeway corridor, a segment of the I–80 Connected Vehicle Testbed in Wyoming. The simulation results show the effectiveness of selected WRMS applications and provide operational insights that state and local transportation agencies may use in future strategic planning and operations of their weather-responsive programs.

Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems

The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode.