Powered Two Wheelers Research Articles

Crash risk associated with powered two-wheeler filtering maneuvers in urban mixed and weak-lane-disciplined traffic streams

The paper studies the crash risk associated with the filtering Powered Two-Wheelers (PTWs) using the trajectory data. A methodology is proposed to automatically identify the filtering PTWs from the trajectory data. A state-of-the-art surrogate safety measure, considering both proximity and evasive actions, was used for identifying the PTW conflicts. The results revealed that the side-swipe conflicts, particularly involving larger vehicles, are predominantly observed during filtering maneuvers. However, the results revealed no significant correlation between the accepted lateral gap and relative speed during PTW filtering. These aspects highlight the hazardous driving behavior of PTW riders while engaging in the filtering maneuvers. The side-swipe crash risk during PTW filtering is about three times higher than the rear-end and side-swipe crash risk during non-filtering. The study suggests that improving infrastructure by implementing designated PTW lanes, where feasible, can reduce the interaction of PTWs with larger vehicles, thereby mitigating the risk of severe crashes.

A modeling method for two-dimensional two-wheeler driving behavior during severe conflict interaction at intersections

The safety of two-wheelers is a serious public safety issue nowadays. Two-wheelers usually have severe conflict interaction with vehicles at intersections, such as running red lights, which is very likely to cause traffic accidents. Therefore, a model of two-wheeler driving behavior in conflicting interactions can provide guidance for traffic safety management on one hand, and can be used for the development and testing of autonomous vehicles on the other. However, the existing models perform poorly when interacting with vehicles. To address the problems, this paper proposes a modeling method (an improved social force model, ISFM) for two-dimensional two-wheeler driving simulation for conflict interaction at intersections. Based on analysis of naturalistic driving study data, when two-wheelers encounter with vehicles, their driving intentions and trajectories can be categorized into two groups, which are yielding and overtaking. Therefore, the vehicle-related social forces are designed to be a set of two forces rather than a repulsion force in original SFM, which is a yielding force based on the relative distance between the two-wheeler and the vehicle, and an overtaking force based on the velocity of the two-wheeler itself. This opens up the possibilities for modeling the multi-modal driving intention of two-wheelers encountering with cross traffic. Based on ISFM, a bicycle model, a powered two-wheeler (PTW) model and a model of a group of PTWs, are then constructed. Compared to the original SFM, ISFM increases the precision of driving intention prediction by 19.7 % (yielding situation) and 25.0 % (overtaking situation), and reduces the root mean square error between simulated and actual trajectories by 7.8 % and 14.8 % on the bicycle model and the PTW model, respectively. Meanwhile, the model of a group of PTWs also performs well. Finally, the results of ablation experiments also validate the effectiveness of the social force designed based on velocity.

Developing improved crash prevention approaches through in-depth investigation of motorcycle crash causation patterns

Despite advancements in road safety, Powered Two-Wheelers (PTWs) remain a vulnerable group with disproportionately high crash rates. This paper presents an in-depth analysis of PTW crashes in six European countries, with a case study of Loss of Control in Curves (LoCC), to address the gap between crash causation and prevention. By examining crash causation factors and their linkage to prevention strategies, the study illustrates various approaches for connecting causes and countermeasures. These approaches, which are applicable to different crash scenarios, include looking forward in the crash causation chains, looking backward, looking at only the last cause (critical events), or the first cause, or following a systemic approach. The research introduces a set of guidelines following the safe system approach, aiming to enhance the understanding of crash prevention among policymakers. The systemic approach to countermeasures, bridges the shortcomings of traditional crash causation studies that may exhibit bias or a narrow focus on “root causes”. The proposed approach emphasizes the need for a comprehensive view of crash scenarios (i.e., considering the entire crash causation chain or multiple causation chains) and ensuring that preventive measures address the full spectrum of the system. It also takes in to account external factors such as cost, benefits, and politics, leading to improved road safety outcomes. The study findings are significant for researchers, since it is a step forward in in-depth crash causation studies, as well as road practitioners and policymakers, in providing a strategic framework for more effective and efficient road safety interventions.

Automating Helmet Usage Detection: A YOLOv8 Based Framework

Abstract: The biggest threat to Powered two-wheeler(PTW) riders is a head injury. With the humongous amount of daily commuters on such PTWs, the risk only increases substantially. In such cases, helmets reduce the risk of head injuries in accidents but not every citizen is keen on their use. In India, where the use of motorcycles is widespread, ensuring helmetuse remains a challenge. This study explores the effectiveness of YOLOv8(You Only Look Once) a state-of-the-art single-stage object detection algorithm for helmet detection on Indian roads [14]. The CNN(Convolutional Neural Networks) based technologyimplements many other efficient, reliable and quick algorithmsto train, validate and predict the object’s detection, segmentationand classification task. We leverage the use of a publicly available Indian helmet dataset from Kaggle, containing 942 images with annotations. This an image dataset of real-life powered two-wheeler riders. The dataset has images and video frames for training on 5 different objects/classes along with annotations that specify thoseobjects. This multi-class approach offers valuable insight intohelmet usage patterns paves shows us the way for real-world applications for automated traffic monitoring. Real-time helmet and number plate detection can become a game changer in trafficmonitoring and can strengthen basic safety laws

An enhanced method for evaluating the effectiveness of protective devices for road safety application

This paper presents an enhanced probabilistic approach to estimate the real-world safety performance of new device concepts for road safety applications from the perspective of Powered Two-Wheeler (PTW) riders who suffer multiple injuries in different body regions. The proposed method estimates the overall effectiveness of safety devices for PTW riders by correlating computer simulations with various levels of actual injuries collected worldwide from accident databases. The study further develops the methodology initially presented by Johnny Korner in 1989 by introducing a new indicator, Global Potential Damage (GPD), that overcomes the limitations of the original method, encompassing six biomechanical injury indices estimated in five body regions. A Weibull regression model was fit to the field data using the Maximum Likelihood Method with boundaries at the 90% confidence level for the construction of novel injury risk curves for PTW riders. The modified methodology was applied for the holistic evaluation of the effectiveness of a new safety system, the Belted Safety Jacket (BSJ), in head-on collisions across multiple injury indices, body regions, vehicle types, and speed pairs without sub-optimizing it at specific crash severities. A virtual multi-body environment was employed to reproduce a selected set of crashes. The BSJ is a device concept comprising a vest with safety belts to restrict the rider’s movements relative to the PTW during crashes. The BSJ exhibited 59% effectiveness, with an undoubted benefit to the head, neck, chest, and lower extremities. The results show that the proposed methodology enables an overall assessment of the injuries, thus improving the protection of PTW users. The novel indicator supports a robust evaluation of safety systems, specifically relevant in the context of PTW accidents.

Influence of car front-end designs on motorcyclists’ trajectory in head-on and side-on-head crashes

Motorcyclists are highly vulnerable road users, and cars are one of their primary crash opponents. This study investigates the influence of car front-end designs on motorcyclist trajectory in head-on and side-on-head crashes. The analysis is based on a dataset of 120 multi-body crash simulations conducted using MADYMO and post-processed with MATLAB. An analysis of 1412 real-world Powered Two-Wheeler (PTW) to car accidents was conducted to determine the most common crash configurations and the associated ranges of the variables, such as vehicle speeds and contact points. Three PTW styles (sport-touring, scooter, and sport) and four car front-end designs (Sport utility vehicle (SUV), Family Car/Sedan (FCR), Roadster (RDS), and Multi-purpose vehicle (MPV)) were considered.The study examined the riders’ thrown distance in both collision types. It was observed that, regardless of the collision type, the head was identified overall as the primary body region coming into contact with the opposing vehicle, followed by the chest and neck. In frontal collisions, an augmented bonnet height corresponded to an increased incidence of head contact, whereas a lower bonnet height resulted in a higher frequency of chest contact. Moreover, the thrown distance depended also on PTW speed, particularly for sport and sport-touring motorcycles. Notably, contact with the car windscreen was only observed at velocities exceeding 60 km/h, whereas impact with the bonnet leading edge occurred exclusively below this threshold. Due to the shielding effect of their PTW’s fairing, scooter riders predominantly experienced no contact with the opposing vehicle. Sport-touring motorcycles exhibited a more vertical trajectory upon ejection, leading to a greater likelihood of overturning and subsequent rearward head impact with the vehicle. In contrast, sport motorcycles tended to forward projections with a high likelihood of chest contact. In the case of lateral impacts, it was observed that vehicles with a more prominent profile, such as SUVs and MPVs, equipped with protruding bumpers, effectively restrained riders. In this case, vehicle speed did not exert a significant influence on the thrown distance. Additionally, the presence of a conspicuous fuel tank and the initial posture of the rider on the PTW played a crucial role in determining the final thrown distance. Due to their upright postures and the absence of a pronounced fuel tank, scooter dummies were thrown further than others, thus causing head contact with the windscreen.These findings highlight the importance of car front-end design and PTW fairings in mitigating riders’ injuries and provide valuable insights to vehicle manufacturers for developing tailored safety measures for riders.

Evaluating the crash risk of powered two-wheelers in urban mixed traffic environments: a conflict threshold perspective

The study investigates the crash risk of powered two-wheelers (PTWs) involved in multiple conflict types, with different vehicle classes constituting a mixed traffic stream. This study uses the extreme value theory to estimate the crash risk after establishing the conflict thresholds for potential rear-end and side-swipe conflicts by accounting for interacting vehicle types. The study considers four vehicle pairs involving PTWs: PTW-PTW, PTW-MThW (motorized three-wheeler), PTW-Car, and PTW-Bus. The study found that the conflict thresholds corresponding to rear-end and side-swipe types increase with the interacting vehicle size. The crash risk is lowest for the PTW-PTW pair (0.315%) in rear-end conflicts, whereas the risk is the highest for the PTW-MThW pair (3.7%) in side-swipe conflicts. The crash risk corresponding to the PTW interacting with other vehicle types is higher than that of the PTW-PTW pair. Hence, the implementation of exclusive PTW lanes could be an effective risk mitigation strategy for PTW-dominant mixed traffic environments.

Exploratory analysis of evasion actions of powered two-wheeler conflicts at unsignalized intersection

The study investigates the braking and steering evasions of powered two-wheelers (PTWs) during severe conflicts observed at an unsignalized intersection. Traffic conflicts were detected using a surrogate safety indicator called anticipated collision time (ACT). Then the peak-over-threshold approach was used to identify the severe conflicts and the evasive actions. Conflicts between right-turning PTWs and through-moving vehicles, through-moving PTWs crossing through-moving vehicles, and merging/diverging PTWs were analyzed using the minimum ACT (ACTmin), maximum deceleration rate (DRmax), maximum yaw rate (YRmax), and time of evasive action (TEA). The evasive actions were classified into five categories: driver/rider error, no-evasion, braking-only, steering-only, and both braking and steering. Analysis reveals that right-turning PTWs experience higher crash risk (0.7 %) than the other movements. PTW riders primarily employ extreme steering maneuvers (greater than 13 degrees/s) to evade conflicts, whereas braking rates lie in the normal ranges (less than 1.5 m/s2). The time of evasive action varies between 2.04 and 2.44 s, with the right-turning PTW riders responding early. Through-moving riders commit errors while evading severe conflicts and perform fewer evasive actions than right-turning and merging/diverging riders. Right-turning riders perform more steering-only evasions than braking-only, whereas the riders involved in the other two conflicts execute more braking-only evasions. These findings suggest that conflict type influences riders' braking and steering responses. Hence, future applications in advanced driver/rider assistance systems and training programs should consider appropriate evasive action strategies for different conflict types.

Impact Area and Speed Effects on Powered Two-Wheeler Crash Fatality and Injury Risk

<div>The primary objective of this study was to evaluate the fatality risk of powered two-wheeler (PTW) riders across different impact orientations while controlling for different opponent vehicle (OV) types. For the crash configurations with higher fatality rate, the secondary objective was to create an initial speed–fatality prediction model specific to the United States. Data from the NHTSA Crash Reporting Sampling System and the Fatality Analysis Reporting System from 2017 to 2020 was used to estimate the odds of the different possible vehicle combinations and orientations in PTW–OV crashes. Binary logistic regression was then used to model the speed–fatality risk relationship for the configurations with the highest fatality odds. Results showed that collisions with heavy trucks were more likely to be fatal for PTW riders than those with other OV types. Additionally, the most dangerous impact orientations were found to be those where the PTW impacted the OVs front or sides, with fatality odds, respectively, four and five times higher than when the OV rear-end was impacted. The high variability in the odds of different crash configurations suggests the importance of considering the impact orientation factor in future injury prediction models. The speed–fatality prediction models developed for head-on and side crashes could provide an initial tool to evaluate the effectiveness of advanced rider assistance systems and other safety countermeasures in the United States, particularly those that result in speed reductions.</div>

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

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

Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

Association of crash potential of powered two-wheelers with the state of traffic stream

Powered two-wheelers (PTWs) are widely used in low- and middle-income countries for short-distance trips. PTWs have been preferred over the other modes because they are relatively faster in densely populated cities, given their smaller size and higher flexibility. Nevertheless, PTWs pose a higher crash risk, especially in mixed, weak lane-disciplined traffic conditions. There is insufficient evidence emphasizing the safety aspects of PTWs and their relationship with the traffic states. The present study aims to fill this gap by performing a proactive safety assessment using a surrogate safety indicator called Anticipated Collision Time (ACT), which captures the conflict types and the overall crash risk. Unlike most Surrogate Safety Measures (SSMs), the ACT considers the evasive actions of the vehicles, which is more critical for the safety assessment of PTWs. Further, this study investigates the PTW crash frequency, severity, exposure, and evasive actions and their relationship with the traffic states, using various indicators derived from ACT. The results imply that the frequency, exposure, and severity of PTW crashes strongly correlate with Area-Density, a measure of traffic crowdedness, and the proportion of PTWs involved in an unsafe situation increased with Area-Density. The most prominent crash type observed for the PTW was the sideswipe, which can be attributed to the unique riding characteristics of PTWs, such as filtering, weaving, and tailgating. It was observed that the coefficient of variation of PTW speed was a significant factor affecting the conflict severity. The PTW riders were found responding early to an unsafe situation in congested and free-flow conditions, which indicates they are more cautious during these traffic states. However, during the capacity conditions, the riders were less vigilant. This study’s findings will be useful for targeted road safety interventions and campaigns to improve PTW safety. Additionally, the knowledge acquired from this study can be used to develop customized advanced riders’ information systems considering the correlation between crash likelihood and traffic state. Also, understanding the most frequent and severe crash types that PTWs involve in can aid in developing customized rider wearables.

How do road infrastructure investments affect Powered Two-Wheelers crash risk?

The drivers of Powered Two-Wheelers (PTWs) pertain to the collective of so-called vulnerable road users. Crashes have scarcely decreased for these roadway users in recent years, whereas among other users, e.g. cars users, they have declined considerably. Meanwhile, the use of PTWs has risen sharply worldwide. This situation adds a further concern to transportation policies and makes evident the need to explore factors involved in PTW crashes. Yet there is a lack of studies specifically about road safety for PTWs. The present study therefore aspires to advance in the knowledge of the factors affecting PTW crashes on interurban roadways, by means of analyzing the effects of some variables not considered previously in this type of studies —mainly economic resources invested in roads—while also accounting specifically for the exposure to risk of PTWs (veh-km), along with relevant variables related to road traffic, the roadway infrastructure, and socioeconomic, meteorological and legislative factors. To this end, and bearing in mind the latest advancements of incorporating unobserved heterogeneity in count data models, different configurations of random parameters negative binomial models for data panels are presented. The realm of study is the network of national roads in Spain, distributed over 43 provinces, and the time period between 2007 and 2015. The results show significant associations for 11 of the variables considered: annual and accumulated investment in construction, expense on maintenance, proportion of motorways, light and heavy vehicle traffic, per capita GDP, age, unemployment rate, price of gasoline, and modification of the demerit point system (DPS). With respect to transport policy implications, the findings provided in this study may serve to monitor the effects of economic resources allocated to road construction and maintenance —along with other measures, such as gasoline prices and DPS—on PTWs safety.

An Advanced Rider-Cornering-Assistance System for PTW Vehicles Developed Using ML KNN Method.

The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.

Non-stationary crash risk modelling of powered two-wheelers using extreme value analysis of surrogate crash events

Evaluating the impact of evasive actions such as braking and steering on the crash risk assessment of vehicles is a scarce endeavor due to the lack of relevant data. This study uses Extreme Value Theory to investigate and model the effect of evasive actions on the sideswipe crash risk of powered two-wheelers (PTWs) moving on multilane rural highways. The crash risk was projected from the observed sideswipe conflicts that were quantified using a surrogate safety indicator called anticipated collision time (ACT). The vehicle trajectory data extracted from traffic videos, collected using an unmanned aerial vehicle, was used as the input for the analysis. The data was denoised using a state-of-the-art trajectory reconstruction method called recursively ensembled low pass filtering. Once the conflicts were identified from the trajectory data, the crash risk models were developed considering five covariates: maximum deceleration rate, maximum yaw rate, and the times spent in decelerating, accelerating, and steering during a sideswipe conflict. These covariates were used to capture the non-stationarity in the traffic conflict extremes. The best performing non-stationary model was selected by comparing the negative log-likelihood values with the stationary-one. The findings suggest that the PTWs experience significant sideswipe crash risk on four-lane (crash risk 0.09%) and six-lane (crash risk 0.17%) highways. The sideswipe crash risk of PTWs increases with the increase in the intensity of braking and steering actions measured in terms of maximum deceleration and yaw rates. Further, this study emphasizes that incorporating the effects of evasive actions in the crash risk estimation and developing non-stationary models could significantly improve the precision of crash frequency estimates. Based on the findings it can be concluded that for the safety improvement of PTWs on multilane highways, lane-restriction should be imposed which can increase the safety margin during sideswipe conflicts.

Stabbed by motorcycle? Reconstruction of an unusual traffic accident

The reconstruction of traffic accidents involving powered two-wheelers (PTWs) frequently proves to be a challenging task. A case in which a fatal head-on crash of a PTW with a small truck where only minor vehicles damage was observed but resulted in isolated fatal chest trauma is discussed here. External examination of the corpse revealed two lacerations on the back, at the first glance implying sharp trauma. Based on the accident traces, the technical expert assumed an emergency break of the PTW rider resulting in a rotation of the PTW in terms of a wheelie on the front wheel. The first contact between the PTW rider and the tail end of the small truck probably occurred with the upper side of the helmet, and then, the back handle of the PTW caused the stab-like injuries followed by compression of the rider between the small truck or asphalt and the PTW. Based on the few accident traces available, neither a reconstruction of the pre-impact velocity nor a detailed reconstruction of the PTW rider kinematics was possible. However, using an interdisciplinary approach, the principal collision position as well as the injury mechanisms could be reconstructed.

Does Motorcycle Autonomous Emergency Braking (MAEB) mitigate rider injuries and fatalities? Design of effective working parameters and field test validation of their acceptability

Autonomous Emergency Braking on Motorcycles (MAEB) was shown to be a promising technology to improve Powered-Two-Wheeler (PTW) user safety, deploying autonomously a braking action to reduce impact speed when pre-crash conditions are detected. The applicability of MAEB with effective working parameters still needs to be proved in real-world working conditions. The goal of this paper is to define MAEB working parameters for effective mitigation of injuries and to validate their real-world applicability through field tests in representative riding scenarios of intervention.The results of previous studies and injury risk functions for motorcyclists were employed to define a set of MAEB working parameters (duration of intervention ≥ 0.60 s, target declaration of 0.5 g, fade-in jerk of 2 g/s) which enable MAEB impact speed reductions capable of mitigating injuries. Field tests were then carried out involving common riders as participants and employing a test protocol consistent with previous studies. Automatic Braking (AB) interventions were intended to reproduce unexpected MAEB activations in different riding conditions including straight-line riding and lane-change manoeuvres reproducing an avoidance action. The tests were carried out using a sport-touring motorcycle provided with extension arms to prevent capsizing and equipped with an automatic braking system which was activated remotely by researchers.Thirty-one participants involved in the test experienced AB interventions riding in straight-line and lane-change at a mean speed ranging from 41 km/h to 47 km/h. In the 0.5 g test session, the AB system performed mean deceleration 0.48 g reached with 2.0 g/s fade-in jerk for a duration of around 1.1 s. Both subjective assessment and vehicle data analysis indicated that the participants were consistently able to easily control the vehicle during the automatic braking interventions and were always able to complete a lane-change manoeuvre. No signal of incipient loss of vehicle control was recorded in on-board videos nor identified in the behaviour and body movement by participants. The tested MAEB working parameters performed speed reductions capable to reduce impact speed up to 15 km/h, potentially reducing serious injuries (MAIS3+) by 15 %.The proposed MAEB working parameters were proved as effective in reducing vehicle speed and feasible from end-users’ perspective. The designed MAEB intervention was shown to be applicable in straight line riding and to allow the rider to manoeuvre the vehicle for the execution of avoidance actions.

Co-Design and Experimental Validation of a Gyroscopic Stabilizer for Powered Two-Wheelers

Roll dynamics of powered two-wheelers (PTWs) are unstable at low speed, and their active control could improve both safety and comfort. This article proposes the co-design of a stabilization system based on control momentum gyroscopes, which, with their compact package and low power request, are a fascinating option in particular for PTW, where onboard energy and size constraints make the design of a proper roll torque actuator challenging. The joint design of the actuator and its control law is proposed by defining a multiobjective optimization problem, which also turns into a tool that allows the designer to steer the parameters choice in different directions. The resulting prototype is manufactured, installed on a motorcycle, tested, and experimentally validated in several conditions.

Factors affecting fatal PTW at-fault crash outcome metrics at intersections and non-intersections in South Korea

Fatal crashes involving Powered Two-Wheelers (PTWs) are a major health concern in South Korea due to the increase in their usage in the logistics industry. Owing to the way, they are operated on the roadways, most riders end up in fatal crashes. Interestingly, little research exists regarding the impact of risk factors on fatal crashes involving at-fault PTW riders. This study employs a copula-based regression technique to simultaneously model the relationship between crash-risk factors and crash outcome metrics of fatal PTW rider-at-fault crashes, namely the number of crash casualties (casualty size) and the number of vehicles involved in a crash (crash size) at intersections and non-intersection segments. The proposed method was superior compared to the SEM-based bivariate regression approach, and the estimation results showed that there exists a positive relationship between both outcome variables. From the analysis, it was identified that while "other violations" comprising speeding and wrongful overtaking had varying effects on crash size outcomes at the intersection and non-intersection segments, variables such as daytime, winter, head-on collisions, and pedestrian involvement had positive impact on the crash consequence metrics irrespective of the crash location. Insights drawn from the study are used in recommending appropriate countermeasures for improving PTW safety.

Autonomous driving testing scenario generation based on in-depth vehicle-to-powered two-wheeler crash data in China

A reliable critical-scenario-based safety assessment of autonomous vehicles in China requires a thorough understanding of complex crash scenarios in Chinese background traffic. Based on actual crashes between a vehicle and a powered two-wheeler (PTW) in China, this study generated the autonomous driving testing scenarios from functional, logical and concrete levels. First, 239 video-recorded crash cases were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. Using the k-medoids clustering method, six functional scenarios were generalized according to seven crash characteristics (time of day, road type, road surface, obstruction, motion of vehicle, motion of PTW, relative moving direction and position of PTW with respect to vehicle), which contained two straight road scenarios, two T-junction scenarios and two intersection scenarios. Then, using a trajectory analysis program written by Python, the dangerous time instant of each crash was extracted based on the relative trajectory. According to five dynamic parameters of dangerous time instant, namely vehicle velocity (Vehicle_V), PTW X'-coordinate velocity (PTW_VX'), PTW Y'-coordinate velocity (PTW_VY'), PTW X'-coordinate relative position (PTW_LocX') and PTW Y'-coordinate relative position (PTW_LocY'), a crash trigger scheme was built to remain a case challenging when the involved vehicle is replaced by an autonomous vehicle with completely different maneuvers. Using the kernel density estimation (KDE), the logical scenarios were evolved by calculating the distribution of these dynamic parameters in each cluster. The results showed that there were differences in the distribution of dynamic parameters between six functional scenarios. For instance, the Vehicle_V in the scenario where a vehicle turning right impacts with a right/right rear PTW traveling straight ahead was higher than that in the scenario where a vehicle changing to the left lane impacts with a left/left rear PTW traveling straight ahead, with ranges of (10 km/h, 30 km/h) and (5 km/h, 15 km/h), respectively. Finally, considering the correlation of dynamic parameters, a virtual crash generation approach based on the independent component analysis (ICA) representing the original crashes with independent parameters was proposed to obtain sufficient concrete testing scenarios. The results showed that the statistical characteristics of virtual crashes were consistent with those of original crashes. Therefore, the virtual crash generation approach was effective. And a concrete crossing testing scenario with the crash trigger conditions of Vehicle_V = 26.272 km/h, PTW_VX' = 15.567 km/h, PTW_VY' = −1.670 km/h, PTW_LocX' = −27.265 m and PTW_LocY' = 52. 149 m was especially demonstrated. This study provides a theoretical basis for generating autonomous driving testing scenarios and data support for establishing relevant testing schemes tailored to the traffic environment in China.