Active Safety Technology Research Articles

Potential of heavy goods vehicle countermeasures to reduce the number of fatalities in crashes with vulnerable road users in Sweden

Heavy Goods Vehicles (HGVs) are involved in a large share of all serious and fatal collisions. Among these, about 30% are collisions involving Vulnerable Road Users (VRUs). The aim of the present study was to evaluate the potential of Heavy Goods Vehicle countermeasures to prevent fatalities with vulnerable road users in Sweden. Both the General Safety Regulation (GSR) and coming Euro NCAP test program were taken into account. Furthermore, elaboration on existing passive HGV safety systems were used to investigate any additive benefit. The Swedish Transport Administration carry out in-depth studies of all road fatalities. All in-depth studies for the period 2015–2020 were analysed retrospectively by a consensus group of three analysts, to assess the effectiveness of 22 active and passive safety systems. For each technology, target populations and boundary conditions were defined in order to facilitate the assessment. In total, 63 fatal crashes were found, compiled of 28 pedestrians, 13 bicyclists and 22 Powered Two Wheelers (PTWs, i.e. motorcyclists and moped riders). Overall, it was found that active and passive safety technologies could prevent up to 59% (37/63) of the included fatalities. For pedestrians, the potential of improved HGV driver vision, both with a surround view system and an improved direct vision, would have the larger potential to save lives. For bicyclists where the turn-right scenario is overrepresented, the implementation of Advanced Emergency Braking in junctions and Blind Spot Information Systems had the highest potential to save lives. For passive safety systems, HGV wheel protection had a potential to save many bicyclists by preventing them from being run over. Crash scenarios involving a PTW are the most challenging to address with HGV safety systems, mostly due to high PTW speed. Nevertheless, wheel protection on the HGV could save the lives of PTW drivers, by preventing them from being overrun. The present study showed that the included active and passive safety technologies for Heavy Goods Vehicles could prevent 59% of fatalities among vulnerable road users in Sweden. The fatalities not targeted by the HGV safety technologies included in the study would need other countermeasures such as connected safety technology (e.g. V2V or V2I), infrastructure, or education.

Investigating the Impacts of Autonomous Vehicles on the Efficiency of Road Network and Traffic Demand: A Case Study of Qingdao, China.

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China's autonomous vehicle industry, at present, the development scale of China's intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region.

Research on Vehicle AEB Control Strategy Based on Safety Time–Safety Distance Fusion Algorithm

With the increasing consumer focus on automotive safety, Autonomous Emergency Braking (AEB) systems, recognized as effective active safety technologies for collision avoidance and the mitigation of collision-related injuries, are gaining wider application in the automotive industry. To address the issues of the insufficient working reliability of AEB systems and their unsatisfactory level of accordance with the psychological expectations of drivers, this study proposes an optimized second-order Time to Collision (TTC) safety time algorithm based on the motion state of the preceding vehicle. Additionally, the study introduces a safety distance algorithm derived from an analysis of the braking process of the main vehicle. The safety time algorithm focusing on comfort and the safety distance algorithm focusing on safety are effectively integrated in the time domain and the space domain to obtain the safety time–safety distance fusion algorithm. A MATLAB/Simulink–Carsim joint simulation platform has been established to validate the AEB control strategy in terms of safety, comfort, and system responsiveness. The simulation results show that the proposed safety time–safety distance fusion algorithm consistently achieves complete collision avoidance, indicating a higher safety level for the AEB system. Furthermore, the application of active hierarchical braking minimizes the distance error, at under 0.37 m, which meets psychological expectations of drivers and improves the comfort of the AEB system.

An investigation of ADAS testing scenarios based on vehicle-to-powered two-wheeler accidents occurring in a county-level district in Hunan province

Abstract To meet the requirements for high-risk testing scenarios in the safety assessment of advanced driver assistance systems (ADAS), this study selected 239 vehicle-to-powered two-wheeler accidents occurring in Xiangtan County from the China In-depth Mobility Safety Study—Traffic Accident (CIMSS-TA) database. Seven scenario elements were selected from the levels of static road elements, dynamic traffic participants and environment elements. The accident characteristics were obtained through statistical analysis. The results showed 75.73% of accidents occurred at T-junctions/intersections, and 34.73% of accidents were caused by the visual obstructions. Six typical vehicle-to-powered two-wheeler crash scenarios were established by cluster analysis, including two straight road scenarios (scenario 3 and 6), two T-junction scenarios (scenario 1 and 5), and two intersection scenarios (scenario 2 and 4). Furthermore, six vehicle-to-powered two-wheeler testing scenarios, which represent the traffic environment characteristics of Chinese county-level districts, were constructed by analyzing the vehicle/PTW speed, the first collision point of vehicle, obstacle type and location, etc. The results showed the vehicle speed in scenario 4 was higher than that in scenario 2, with the average values of 34.32 km/h and 21.99 km/h, respectively. The results of this study provided testing scenarios for the safety assessment of ADAS, and contributed to improving the vehicle active safety technology.

Stability analysis and numerical simulation of a car-following model considering safety potential field and V2X communication: A focus on influence weight of multiple vehicles

In a V2X environment, the target vehicle is capable of acquiring motion information from multiple vehicles ahead, and this information plays a crucial role in predicting the target vehicle's motion behavior. To better understand how leading vehicles affect the car-following behavior of the target vehicle, we have developed an improved car-following model that considers the effects of multiple leading vehicles. This model constructs distance-based and field-based models to describe the influence weights of different vehicles on the target vehicle. The stability conditions of the model were obtained through linear stability analysis, and the mKdV equation, which describes the evolution characteristics of traffic density waves in congested areas, was determined through nonlinear analysis. Numerical simulations were conducted to discuss the multi-vehicle effect λ, different vehicle influence weights (distance-based and field-based models) βj, and the number of leading vehicles considered q. The study found that in congested areas, initial perturbations evolve in the form of kink-antikink waves, moving rearward, with the amplitude of the headway curve decreasing as the multi-vehicle effect coefficient value increases. For different vehicle influence weights, the field-based model outperforms the distance-based model. Moreover, as the number of leading vehicles considered q increases, the stability of traffic flow gradually improves. The numerical results are consistent with the theoretical findings, and it is noted that the model successfully enhances vehicular movement efficiency, reduces congestion, and improves road safety. To minimize collision incidents, the improved model can be implemented as an active safety technology.

Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.

A review of safety test methods for new car assessment program in Southeast Asian countries

Abstract Vehicles with advanced active safety technology can decrease the significant traffic accidents that can lead to death. This active safety frontier falls under primary safety in the European New Car Assessment Program (Euro NCAP) 2025 Roadmap, which has become one of the overall safety rating initiatives toward safer vehicles. Some frontier active safety technologies will be assessed, including autonomous emergency steering (AES) and autonomous emergency braking (AEB). However, the New Car Assessment Program in Southeast Asian Countries (ASEAN NCAP) only focuses on AEB technologies. Hence, this work discusses the existing papers on AES assessment, AES demand, AES control, AES system with Artificial intelligence, and AES testing methodology. Three articles from the industry discussing the AES function in passenger automobiles were found as a result of an article search using the Google search platform. Other terminologies like emergency steering control and emergency steering assist are used instead of AES. However, the principle remains the same. The three categories have been recognized from all of the document results: road adhesion condition, driver condition identification, and rear-end collision. However, only the rear-end collision situations are further investigated in this work to recognize the currently available approach used by previous studies. According to the review findings, just a few AEB intervention systems are now accessible, while AES technology is still in its early phases. That might explain the lack of exact evaluations and effective remedies. As a result, this research aims to offer evidence supporting the proposed methodology for assessing and evaluating AES in the ASEAN NCAP rating scheme. Besides that, this study can also help industries such as automakers and automotive vendors leverage the guidelines to fit the AES in their future models.

Increasing the Safety of the Car

Road safety has improved significantly in recent years thanks to effective action taken at European and national level with changes in legislation related to improving driver and road training. As a result, EU roads are some of the safest in the world. The EU has introduced mandatory fitment of electronic stability control systems on all vehicles and advanced emergency braking and lane departure warning systems, contributing to a reduction in fatalities. Modern active safety technologies are increasingly being implemented in vehicles. The purpose of this report is to present a complex of systems, their functionality, benefit and significance for increasing road safety.

Planning and control of drifting-based collision avoidance strategy under emergency driving conditions

Automated emergency collision avoidance (CA) technology has made tremendous progresses in recent years. However, the existing CA approaches, either braking or steering based, cannot handle well the extremely emergency CA scenarios with limited space and time. This is a research gap to be bridged before the zero-fatality vision of road traffic, namely Vision Zero, can be realized. To further extend the safety limits of vehicles, especially that of automated vehicles in future, we propose a drifting-based CA approach that adopts an aggressive, but controllable, over-steering maneuver. To this end, first the trajectory planning of drifting is formulated and solved as a constrained optimal control problem, which considers vehicle dynamics, actuator limits and collision constraints. Then the knowledge of the three CA approaches’ capability limits is obtained and used for CA decision. Given a planned trajectory, a feedback linearization based controller is further designed to complete the drifting maneuver. Finally, using a real vehicle with by-wire actuators, a series of simulations and road tests are carried out. Results show that our proposed drifting-based algorithm can successfully fulfill agile CA tasks in extreme conditions. With further improvements, this preliminary and conceptual work may contribute as a promising complement to current active safety technologies.

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.

Improved Car-Following Model for Connected Vehicles Considering Backward-Looking Effect and Motion Information of Multiple Vehicles

To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms.

Which not-at-fault crashes are unavoidable by using current active safety technology?

Previous research has focused on analysing crash reduction potential of active safety technology in at-fault passenger cars, but only a few studies have examined counterparties' possibilities to avoid collisions by using advanced driver assistance systems (ADAS). This study quantified the incidence of fatal not-at-fault passenger car crashes that current ADAS (up to SAE level 2) would be unable to avoid. We used data taken from in-depth investigated fatal crashes in which a passenger car was involved, that car having been first registered during the period 1 January 2010 to 31 December 2017 in Finland. The evaluation of unavoidable crashes consisted of two evaluation rounds. The preliminary evaluation round identified potential active safety systems that could have operated in the studied cases. In the following round, we made a five-level case-by-case analysis including a time headway analysis in order to evaluate the possibilities for crash avoidance. The crash data included 63 fatal crashes, of which five were excluded because the death was due to a sudden illness attack. The remaining 58 crashes were classified as follows: probably unavoidable (n = 51), avoidable (n = 3), and unclear (n = 4). The crash incidence of the unavoidable not-at-fault party crashes was 0.67–0.73 fatal crashes per billion kilometers and 14–15 fatal crashes per million registration years. The results indicate that current active safety systems may be able to prevent not-at-fault party fatal crashes only in a few cases and that the driver's role in road safety remains important despite the deployment of the active safety systems.

Real-Time Center of Gravity Estimation for Intelligent Connected Vehicle Based on HEKF-EKF

The vehicle center of gravity estimation is the key technology to the vehicle active safety system in intelligent connected vehicles. In this study, an integrated estimation approach for center of gravity (CG) combining Huber Extended Kalman Filter and Extended Kalman Filter (HEKF-EKF) is proposed. First, HEKF algorithm is used to estimate the distance between the CG and the front axle at the current time. Then, the CG height obtained by HEKF and EKF algorithms is weighted to obtain the optimal estimate value. Finally, the results show that the algorithm’s estimation convergence time is 2 s, its longitudinal position estimation error is less than 2%, and its center of gravity height estimation error is less than 3%. The longitudinal and vertical positions of the vehicle CG can be accurately estimated using this method. This method can help advance the development of active safety technology.

Modeling and stability analysis of car-following behavior for connected vehicles by considering driver characteristic

Research of car-following behavior is very important to alleviate traffic congestion and ensure traffic safety. To describe the car-following behavior of connected vehicles and restore the real driving environment, this paper presents an improved car-following model for connected vehicles by considering driver characteristic and speed information of multiple vehicles, which reflects the interaction between adjacent connected vehicles and the influence of driver characteristic on the stability of car-following. The linear stability analysis and nonlinear stability analysis prove that the improved model is effective and can promote the stability of traffic flow more than other models. In addition, the numerical simulations show that the new model performs better in eliminating disturbances than other models. Finally, the parameters of proposed model are calibrated based on NGSIM data. The results show that the new model performs well in car-following process, keeping a safe distance from the leader vehicle without sudden acceleration and deceleration. The new model also can predict the acceleration behavior of the leader vehicle after emergency brake. In summary, the model proposed in this paper can be used as active safety technology to prevent collision accidents, or as a user-defined function or model in the traffic simulation software, or as car-following strategy in driverless algorithms.

Application of Digital Twin in Handling and Transportation of Hazardous Chemicals

In China, the Ministry of Transport stressed the need to “Strengthen the application of active safety technology”. The transportation of hazardous chemicals represented by LNG, LPG, and liquefied hydrocarbons is the weak link in traffic safety. The aim of this study is to apply digital twin (hereinafter referred to as DT) technology to the whole process of handling (including loading and unloading) and transportation of hazardous chemicals to help improve the anti-risk ability of road networks at all levels. The method is intended to design a monitoring system covering operation visualization, information fusion, cargo tracking, and hazard source monitoring that is based on DT technology and multi-source data acquisition technology. First, DT technology in the areas of hazardous chemicals handling and transportation is discussed. Then, the DT system is designed, including the system construction, functions, and the means of achieving these functions. Finally, taking the procedure in LNG road transportation as an example, we illustrate the application of DT in its four stages. This system is used to present the evolutionary path of accidents that occur in different links and assist in testing the rationality of the comprehensive disposal plan.

Fasting the stabilization response for prevention of tractor rollover using active steering: Controller parameter optimization and real-vehicle dynamic tests

The rollover problem is prominent for a small tractor under complicated road conditions. The application of active steering (AS) technology is an effective approach to solve this problem. A lateral stability index based on the critical position is proposed and improved. Sliding mode control (SMC) technology based on an exponential terminal sliding mode surface is studied by considering the parameter changes and external disturbances in the rollover process of the tractor, which further accelerates the system’s response speed. A real vehicle test platform was built taking into account the limitations of the few real vehicle dynamic tests in current studies. Real vehicle dynamic tests with varying speeds, slopes, and obstacles were conducted for various road pavements and typical off-road pavements. For road pavements, the results demonstrated that the maximum roll angle of the tractor was 15.94 % less than with that under uncontrolled. For typical off-road pavements, AS control effectively restrained rollover accidents under non-extreme conditions, and SMC reduced the time the tractor was in the critical rollover region by 35.98 % on average compared with PID control. The stability region boundary of a small tractor was defined, and the stable driving region of a tractor on various slopes is obtained. This study provides a specific theoretical basis and implementation plan for guiding the research of tractor active safety technology.

Research on Active Safety Methodologies for Intelligent Railway Systems

Safety is essential when building a strong transportation system. As a key development direction in the global railway system, the intelligent railway has safety at its core, making safety a top priority while pursuing the goals of efficiency, convenience, economy, and environmental friendliness. This paper describes the state of the art and proposes a system architecture for intelligent railway systems. It also focuses on the development of railway safety technology at home and abroad, and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems (CPS), data-driven models, and intelligent computing. Finally, several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction.

With the development of technology, vehicle trajectory prediction and safety decision technology has become an important part of active safety technology. Among them, the vehicle trajectory prediction technology can predict the vehicle position, speed, and other motion states in the predicted period according to the current and historical vehicle running state, and the prediction results can provide support for judging the vehicle safety in the predicted period. In order to analyze the above problems, this study fully extracted the main feature information from the vehicle lane change track data with the help of the powerful nonlinear learning and high pattern recognition ability of support vector machine, and conducted identification modeling for the actual lane change process of the vehicle and predictive analysis of the vehicle lateral movement track. First, the lane-changing behavior of vehicles was analyzed, and the vehicle lane-changing execution stage and 10 influencing factors that could characterize lane-changing behavior were determined based on NGSIM to extract the data of lane-change-related variables. Then, a lane-changing recognition model based on gridsearch-PSO is proposed. In the Matlab environment, the model has a test accuracy of 97.68%, while the SVM model without optimization parameters has a recognition accuracy of only 80.87%. The results show that the model has strong classification ability and robustness. Finally, by using the polynomial model for lateral movement trajectory fitting, K-fold cross-validation method is used for fitting polynomial model fitting test.

Road Adhesion Coefficient Identification Method Based on Vehicle Dynamics Model and Multi-Algorithm Fusion

<div class="section abstract"><div class="htmlview paragraph">As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model. The square root cubature Kalman filter algorithm is used to remove the noise of the input of the neural network model, and Q-learning is used to strengthen learning, and the weight coefficient and bias coefficient of the model are continuously rewarded and punished, so that the predicted value does not exceed the normal range of values. The algorithm was pre-trained through CarSim/Simulink co-simulation, 9 sets of simulation conditions were established, and 4 sets of verification schemes were designed for identification and inspection. The average error of the simulation process is 4.93%, and the accuracy is 91.23%. Compared with the traditional Elman neural network, the average recognition error of this method is reduced by 2.23%, and the accuracy rate is increased by 9.83%. Real vehicle experiments were carried out on wet asphalt pavement and dry asphalt pavement, verifying the feasibility of the method. This paper proposes a road adhesion coefficient recognition method, which can improve the applicability of intelligent vehicle active safety systems to complex scenarios.</div></div>

STABILITY CONTROL OF ENERGY SAVING ELECTRIC VEHICLE USING DYNAMIC NONLINEAR SYSTEM STATE ESTIMATION

The purpose is to study the handling stability and energy saving mode of energy saving electric vehicles. First, the differences between electric vehicles and traditional fuel vehicles are analyzed. Moreover, based on active safety technology, the control stability, handling stability and driving safety of electric vehicles are improved. Then, based on the vehicle dynamics theory and the proposed assumptions, the modular electric vehicle dynamics model is established, including the wheel model, tire model and driver model, which can effectively control the error of the vehicle model and reduce the difficulty and complexity of vehicle modeling. Next, based on the model predictive control theory, the vehicle steady-state control strategy is established, which can predict and control the state of the electric vehicle in the compound condition and improve the control stability of the vehicle. Finally, the vehicle dynamic model and steady-state control system are simulated and tested. The experimental results show that the simulation results of the established dynamic model of the electric vehicle in angle step condition and single line shifting condition are consistent with the results of professional software, which shows that the established dynamic model can accurately describe the dynamic state of electric vehicle and provides the basis for the research of vehicle stability control strategy. Experimental results of the control strategy under the condition of double line shifting show that the designed control strategy can accurately track the state of the system and constrain the deviation in a small range, so as to ensure the driving stability of the electric vehicle. This exploration provides a reference for the study of dynamic analysis and handling stability control strategy of electric vehicles.