Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

Effect of Weight Distribution and Active Safety Systems on Electric Vehicle Performance.

This paper describes control methods to improve electric vehicle performance in terms of handling, stability and cornering by adjusting the weight distribution and implementing control systems (e.g., wheel slip control, and yaw rate control). The vehicle is first simulated using the bicycle model to capture the dynamics. Then, a study on the effect of weight distribution on the driving behavior is conducted. The study is performed for three different weight configurations. Moreover, a yaw rate controller and a wheel slip controller are designed and implemented to improve the vehicle's performance for cornering and longitudinal motion under the different loading conditions. The simulation through the bicycle model is compared to the experiments conducted on a rear-wheel driven radio-controlled (RC) electric vehicle. The paper shows how the wheel slip controller contributes to the stabilization of the vehicle, how the yaw rate controller reduces understeering, and how the location of the center of gravity (CoG) affects steering behavior. Lastly, an analysis of the combination of control systems for each weight transfer is conducted to determine the configuration with the highest performance regarding acceleration time, braking distance, and steering behavior.

Beyond 1D and oversimplified kinematics: A generic analytical framework for surrogate safety measures.

This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicle dynamics of differing dimensionality and fidelity, and suitable for dynamic, real-world environments. The framework incorporates a generic vehicle movement model, accommodating a spectrum of scenarios with varying degrees of complexity and dimensionality, facilitating the estimation of future vehicle trajectory evolution. It establishes a generic mathematical criterion to denote potential collisions, characterized by the spatial overlap between a vehicle and any other entity. A collision risk is present if the collision criterion is met at any non-negative estimated future time point, with the minimum threshold representing the remaining time to collision. The framework's proficiency spans from conventional one-dimensional (1D) SSMs to extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated through simulation experiments that assess the precision of the framework when linearization is performed on the vehicle movement model. The outcomes showcase remarkable accuracy in estimating vehicle trajectory evolution and the time remaining before potential collisions occur, comparing to high-accuracy numerical integration solutions. The necessity of higher-dimensional and higher-fidelity SSMs is highlighted through a comparison of conventional 1D SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D SSMs over 3D SSMs could be off by 300% for Time-to-Collision (TTC) values larger than 1.5 s and about 20% for TTC values below 1.5 s. In other words, conventional 1D SSMs can yield highly inaccurate and unreliable results when assessing collision proximity and substantially misjudge the count of conflicts with predefined threshold (e.g., below 1.5s). Furthermore, the framework's practical application is demonstrated through a case study that actively evaluates all potential conflicts, underscoring its effectiveness in dynamic, real-world traffic situations.

Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis.

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.

Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction.

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal, the accident can be avoided or not by the driver when reacting to this signal.

Optimization of Forward Collision Warning Algorithm Considering Truck Driver Response Behavior Characteristics.

Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers' responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms' minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify driver response behavior into three categories: Response Before Warning (RBW), Response After Warning (RAW), and No Response (NR). Results showed that RBW clusters tend to occur at long following distances (>19 m), and drivers applied braking before the warning. In RAW clusters, deceleration after warning is significantly more forceful than before warning. NR clusters occur at short distances, and deceleration fluctuates only slightly. To optimize the FCW algorithm, the warning distance was divided into reaction distance and braking distance. The linear support vector machine was used to fit the driver reaction distance. The long short-term memory method was used to predict braking distance based on each of the three response scenarios: R2 was 0.896 for RAW scenarios, 0.927 for RBW scenarios, and 0.980 for NR scenarios. Verification results show that the optimized truck FCW algorithm improved safety by 1 % to 5.1 %; accuracy reached 97.92 %, and the false alarm rate was 1.73 %.

Modeling collision avoidance maneuvers for micromobility vehicles.

INTRODUCTION: In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have also arisen. There is an urgent need for validated models that comprehensively describe the behavior of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision-avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance-braking and steering-maneuvers. METHOD: We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering. RESULTS: Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models. CONCLUSIONS: This study demonstrates that the new micromobility solutions have some maneuverability characteristics that differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision-avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modeling for MMV kinematics can be beneficial but needs validation. PRACTICAL APPLICATIONS: The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations.

Holistic pedestrian safety assessment for average males and females.

Objective: An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters. Methods: The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures. Results: In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male. Conclusions: A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Evaluation of intersection crashes using naturalistic driving data through the lens of future I-ADAS.

OBJECTIVE: Intersection advanced driver assistance systems (I-ADAS) with the capability to detect possible collisions and perform evasive braking have the potential to reduce the number of intersection crashes. However, these systems will encounter many challenges caused by the complexity of real-world driving conditions. The purpose of this study is to use real-world naturalistic driving data to conduct an initial exploration of the potential challenges for future I-ADAS in straight crossing path (SCP), left turn across path/lateral direction (LTAP/LD), and left turn across path/opposite direction (LTAP/OD) crash configurations. METHODS: Intersection crashes were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. The SHRP 2 dataset includes front-facing, driver-facing, rear-facing, and a hands/feet-facing video and vehicle speed, steering, accelerator, and brake time-series data. This data was reviewed to understand driver sightline obstructions, driver distractions, and initiation of driver responses. The estimated time to collision (TTC) from the precipitating event, defined as when either vehicle entered the intersection without the right-of-way, was computed based on the distance to the impact point divided by the current velocity of the subject vehicle. RESULTS: The median impact speed was 18.0 km/h for SCP and LTAP/LD crashes and 16.1 km/h for LTAP/OD crashes. The median TTC from the precipitating event was 1.35 s for SCP and LTAP/LD crashes and 1.44 s for LTAP/OD crashes. For SCP crashes, the three main sightline obstruction scenarios were slower vehicles traveling in the same direction waiting to turn right, vehicles in the closer crossing lane, and a parked truck. For LTAP/OD crashes, the sightline obstruction was often oncoming vehicles in a closer lane blocking the view of another vehicle. CONCLUSION: Sightline obstructions could present a challenge for future I-ADAS to activate in SCP, LTAP/LD, and LTAP/OD crashes. This study utilized naturalistic driving data to complete a comprehensive analysis of intersection crashes, including driver distractions, evasive maneuvers, and sightline obstructions that can assist in the development of I-ADAS. This analysis is not possible with police-reported crash data only, which does not contain necessary details on the driver and surrounding environment.

How do different micro-mobility vehicles affect longitudinal control? Results from a field experiment.

INTRODUCTION: While micromobility vehicles offer new transport opportunities and may decrease fuel emissions, the extent to which these benefits outweigh the safety costs is still uncertain. For instance, e-scooterists have been reported to experience a tenfold crash risk compared to ordinary cyclists. Today, we still do not know whether the real safety problem is the vehicle, the human, or the infrastructure. In other words, the new vehicles may not necessarily be unsafe; the behavior of their riders, in combination with an infrastructure that was not designed to accommodate micromobility, may be the real issue. METHOD: In this paper, we compared e-scooters and Segways with bicycles in field trials to determine whether these new vehicles create different constraints for longitudinal control (e.g., in braking avoidance maneuvers). RESULTS: The results show that acceleration and deceleration performance changes across vehicles; specifically, e-scooters and Segways that we tested cannot brake as efficiently as bicycles. Further, bicycles are experienced as more stable, maneuverable, and safe than Segways and e-scooters. We also derived kinematic models for acceleration and braking that can be used to predict rider trajectories in active safety systems. PRACTICAL APPLICATIONS: The results from this study suggest that, while new micromobility solutions may not be intrinsically unsafe, they may require some behavior and/or infrastructure adaptations to improve their safety. We also discuss how policy making, safety system design, and traffic education may use our results to support the safe integration of micromobility into the transport system.

Research on Recognition of Road Hypnosis in the Typical Monotonous Scene.

Road traffic safety can be influenced by road hypnosis. Accurate detection of the driver's road hypnosis is a very important function urgently required in the driver assistance system. Road hypnosis recurs frequently in a certain period, and it tends to occur in a typical monotonous scene such as a tunnel or a highway. Taking the scene of a tunnel or a highway as a typical example, road hypnosis was studied through simulated driving experiments and vehicle driving experiments. A road hypnosis recognition model based on principal component analysis (PCA) and a long short-term memory network (LSTM) was proposed, where PCA was used to extract various parameters collected by the eye tracker, and the LSTM model was constructed to identify road hypnosis. The accuracy rates of 93.27% and 97.01% in simulated driving experiments and vehicle driving experiments were obtained. The proposed method was compared with k-nearest neighbor (KNN) and random forest (RF). The results showed that the proposed PCA-LSTM model had better performance. This paper provides a novel and convenient method to realize the driver's road hypnosis detection function of the intelligent driver assistance system in practical applications.

A real-time driver fatigue identification method based on GA-GRNN.

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.

Assessment of the effect of motorcycle autonomous emergency braking (MAEB) based on real-world crashes.

Objective: Vehicles are increasingly being equipped with Autonomous Emergency Braking (AEB) and literature highlights the utility to fit a similar active safety system in Powered Two-Wheelers (PTWs). This research attempts to analyze the efficacy of PTW Autonomous Emergency Braking (MAEB) when functioning solely, and in the case where both the PTW and Opponent Vehicle (OV) have AEB installed.Methods: 23 crashes involving motorcyclists that occurred in metropolitan areas of Italy between 2009 and 2017 were selected. The "In-depth Study of road Accidents in FlorencE (InSAFE)" provides data for the study. Each crash was reconstructed in PC-Crash 12.1 software. The obtained simulation of the crash dynamics was then used to create the dataset of cases fitted with AEB and MAEB systems. A custom MAEB system was implemented with specifications based on literature.Results: The majority of crashes occurred on urban roads, at intersections, on dry asphalt, with clear visibility, and in daylight. The passenger vehicle was the most frequent opponent vehicle (70%). Almost half the sample involved the PTW rider traveling beyond the speed limit permitted on urban roads. MAEB was found to be applicable in 19 out of 23 real-world crashes allowing the avoidance of two crashes with the progressive triggering criteria (Time to Collision (TTC) - 1.0 s) and one crash in the case where both the PTW and OV have AEB installed with more conservative setups. MAEB simulations show important trends in the reduction of the PTW impact speed (ISR) from the conservative (TTC-0.6s) to standard (TTC-0.8s) to progressive (TTC-1.0s) triggering criteria. The mean impact speed reduction (ISR) becomes 8.6 km/h, 13.8 km/h, 19.1 km/h, respectively.Conclusions: The results suggested that MAEB may be extremely effective in the PTW impact speed reduction and that an earlier MAEB intervention is beneficial in achieving higher reductions in the PTW impact speed. Further, the effect of opponent vehicles also possessing AEB was studied, and it was found that this increased the likelihood of crash avoidance and greater reduction in crash severity in unavoidable circumstances.

Motorcycle curve assist: A novel approach based on active speed control for crash injury reduction.

OBJECTIVE: Safely negotiating curves with a powered-two-wheeler (PTW) requires a high level of skill, and a significant proportion of PTW crashes have a curve involvement. This study aimed to estimate the applicability, potential benefits and feasibility of novel Motorcycle Curve Assist (MCA). The system is designed to operate an emergency control of the speed of a motorcycle approaching a bend at an inappropriate speed. METHODS: First, the MCA system intervention was defined. Second, the applicability of the system and an estimate of its potential benefits was performed based on a PTW crash database. Motorcyclists' injury risk estimates, MCA working parameters and timing of intervention were employed to estimate the potential injury reduction of applicable crash types. Third, a field test campaign involving 29 common riders as participants was conducted to investigate the real-world applicability and acceptability among end-users of the system deployment in one relevant riding condition adopting a range of parameters of intervention. RESULTS: In the crash database, 23% of cases had curve involvement and after detailed analysis, 14% resulted to be suitable for MCA (60% of cases with curve involvement). The potential relative injury risk reduction considering only the benefits due to crash speed reduction ranged from 3-9% for MAIS2+ to 9-27% for MAIS3+ injuries. Field tests were performed in corners approached at an average speed of 28.7 km/h and an average lean angle of 20°. The system provided a mean deceleration of 0.33 g reached with a fade-in jerk of 1.73 g/s, for an average total duration of 0.59 s. For the field test component, participants reported good controllability of the system, with no incipient loss of control recorded nor reported by participants. CONCLUSIONS: The proposed approach for MCA implementation showed considerable potential benefits in terms of injury reduction. The intervention with the defined working parameters was considered feasible by a sample of end-users. When integrated with an intervention logic capable of predicting emergency situations while approaching curves, MCA will be a technology capable of assisting PTW riders in conditions where other available active safety systems do not.

Safety and Tolerability of Ivermectin and Albendazole Mass Drug Administration in Lymphatic Filariasis Endemic Communities of Tanzania: A Cohort Event Monitoring Study.

Ivermectin and albendazole (IA) combination preventive chemotherapy to all at-risk populations is deployed to eliminate lymphatic filariasis. Although safety monitoring is imperative, data from Sub-Saharan Africa is scarce. We conducted a large-scale active safety surveillance of adverse events (AEs) following IA mass drug administration (MDA) to identify the type, incidence, and associated risk factors in Tanzania. After recording sociodemographic, clinical, and medical histories, 9640 eligible residents received single-dose IA combination preventive chemotherapy. Treatment-associated AEs were actively monitored through house-to-house visits on day 1, day 2, and day 7 of MDA. Events reported before and after MDA were cross-checked and verified to identify MDA-associated AEs. 9288 participants (96.3%) completed the seven-day safety follow-up, of whom 442 reported 719 MDA-associated AEs. The incidence of experiencing one or more type of MDA-associated AE was 4.8% (95% CI = 4.3−5.2%); this being significantly higher among those with Pre-MDA clinical events than those without (8.5% versus 4.1%, p < 0.001). AEs were mild (83.8%), moderate (15.9%), and severe (0.3%), and most resolved within 72 h. The incidence of experiencing one, two, ≥ three types of AEs were 2.8%, 1.3%, and 0.6%, respectively. The most common AEs were headache (1.23%), drowsiness (1.15%), fever (1.12%), and dizziness (1.06%). A chronic illness, or clinical manifestation of lymphatic filariasis, or being female or pre-existing clinical symptoms were independent significant predictors of AEs. IA combination preventive chemotherapy is safe and tolerable, and associated AEs are mild-to-moderate and transient, with few severe AEs. Safety monitoring during MDA campaigns in individuals with underlying clinical conditions is recommended for timely detection and management of AEs.

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.

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.

Making a few talk for the many - Modeling driver behavior using synthetic populations generated from experimental data.

Understanding driver behavior is the basis for the development of many advanced driver assistance systems, and experimental studies are indispensable tools for constructing appropriate driver models. However, the high cost associated with testing is a serious obstacle in collecting large amounts of experimental data. This paper presents a methodology that can improve the reliability of results from experimental studies with a limited number of participants by creating a virtual population. Specifically, a methodology based on Bayesian inference has been developed, that generates synthetic cases that adhere to various real-world constraints and represent possible variations of the observed experimental data. The application of the framework is illustrated using data collected during a test-track experiment where truck drivers performed a right turn maneuver, with and without a cyclist crossing the intersection. The results show that, based on the speed profiles of the dataset and physical constraints, the methodology can produce synthetic speed profiles during braking that mimic the original curves but extend to other realistic braking patterns that were not directly observed. The models obtained from the proposed methodology have applications for the design of active safety systems and automated driving, demonstrating thereby that the developed framework has great promise for the automotive industry.

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking.

Autonomous Emergency Braking (AEB) was proved to be an effective and reliable technology in reducing serious consequences of road vehicles crashes. However, the feasibility in terms of end-users' acceptability for the AEB for motorcycles (MAEB) still has to be evaluated. So far, only Automatic Braking (AB) activations in straight-line motion and decelerations up to 2 m/s2 were tested with common riders. This paper presents a procedure which provides comprehensive support for the design of new experiments to further investigate the feasibility of MAEB among end-users. Additionally, this method can be used as a reference for designing tests for other advanced rider assistance systems.•A comprehensive literature review was carried out to investigate previous findings related to MAEB. After that, a series of pilot tests using an automatic braking device on an instrumented motorcycle were performed.•The specifications for new AB experiments were defined (in terms of test conditions, participants requirements, safety measures, test vehicles and instrumentation).•A test protocol was defined to test the system in different riding conditions and with different AB working parameters. A proposal for the data analysis was presented.

Motorcycle Autonomous Emergency Braking (MAEB) employed as enhanced braking: Estimating the potential for injury reduction using real-world crash modeling.

OBJECTIVE: Recent field-tests on Motorcycle Autonomous Emergency Braking system (MAEB) showed that higher levels of deceleration to improve its effectiveness were feasible. However, the potential of MAEB in mitigating rider injuries is not well understood, particularly in scenarios where the efficacy of standard MAEB is limited because the rider is manually braking. The purpose of this study was first, to assess the injury mitigation potential of MAEB and second, to test MAEB as an enhanced braking system applied in circumstances where the rider is braking before a crash. METHODS: Data from previously investigated motorcycle injury crashes that occurred on public roads in Victoria, Australia were reconstructed using a 2D model. The intervention of MAEB was applied in the simulations to test both MAEB standard and MAEB working as enhanced braking system. The effects of MAEB in mitigating crashes were separated by crash configuration and evaluated based on the modeled reductions in impact speed and injury risk, employing injury risk functions available in the literature. RESULTS: After modeling was applied, MAEB was found to be applicable in 30 cases (91% of those in which was estimated as "possibly applicable"). The modeled Impact Speed Reduction (ISR) among the 30 cases averaged 5.0 km/h. In the cases without manual braking, the mean ISR due to standard MAEB was 7.1 km/h, whereas the relative injury risk reduction ranged from 10% for MAIS2+ to 22% for fatal injuries. In the 14 cases with manual braking, the modeled application of MAEB as enhanced braking led to an average ISR ranging from 5.3 km/h to 7.3 km/h. This resulted in an injury risk reduction ranging from 9% to 12% for MAIS2+ and from 16% to 21% for fatal injuries, depending on the different modes of MAEB. CONCLUSIONS: This study modeled the potential benefits of the highest levels of intervention for MAEB field-tested to date. The findings estimate the degree to which MAEB could mitigate motorcycle crashes and reduce injury risks for motorcyclists. New strategies for MAEB intervention as enhanced braking were modeled through crash simulations, and suggest improvements in the benefits of MAEB when riders are braking before the crash. This highlighted the requirement to perform new field-based tests to assess the feasibility of MAEB deployed as enhanced braking system.