Path selection for the transport of hazardous materials (Hazmats) is a multi-facet decision problem that needs to account for multiple factors such as accident risk as well as transportation cost. Most existing literature has modeled the risk of Hazmats transportation as the product of accident loss, and its probability-based expected utility theory, however, could be problematic since such a risk definition does not necessarily reflect the real perceived risk by the decision-maker. This article proposes a novel approach to the path selection of Hazmats transportation based on the cumulative prospect theory (CPT). Specific steps in the decision of path selection are first laid out in the framework of CPT. Value (Loss) functions of accident in Hazmats transportation are then derived, together with the decision weighting function reflecting accident probabilities. For illustration, a case study is conducted using transportation data from a Hazmats transportation firm in Shanghai. Comparisons of path selections among the newly proposed approach, the existing methods based on expected utility theory, and the actual outcome from the decision-makers clearly indicate the superior performance of the proposed method. The results will enhance the safety level of road transportation of Hazmats.
As a consequence of the introduction of mathematical human body models (HBMs) in consumer information programs, there is an increased need for reliable methods that can demonstrate and build trust in the capability of HBMs to predict human response and injury risk in crashes. Therefore, a framework for validation of strain-based injury prediction is proposed. The frame-work comprises stepwise validation with the final step to validate the utility of risk predictions by means of the area under the curve (AUC) combined with Brier scores. SAFER HBM V1110 previously validated at component and body part levels was selected for the demonstration of the final step of the framework to validate the capability to predict fracture risk in frontal, oblique, and lateral loading. For frontal loading, five postmortem human surrogate (PMHS) test series with 43 PMHS (age range: 19-88 years) were reconstructed. The predicted rib fracture risk for 2+ and 3+ fractured ribs was compared to the number of fractured ribs sustained by the PMHS. Using the framework, the SAFER HBM was for frontal impact analysis found capable of predicting 2+ fractured ribs with an AUC of 0.90 and 3+ fractured ribs with an AUC of 0.89. For oblique and lateral impacts, three PMHS test series including 47 PMHS (23 with 0, 22 with 2+, and 20 with 3+ fractures) were reconstructed, and SAFER HBM rib fracture risk predictions obtained AUC values of 0.84 and 0.87 for 2+ and 3+ fractured ribs, respectively. In the frontal load case, the Brier score was 0.14 for the Number of Fractured Ribs (NFR) 2+ model and 0.17 for the NFR 3+ model. In the lateral/oblique load case, Brier scores were 0.20 and 0.18 for the NFR 2+ and NFR 3+ models, respectively. The proposed framework is a suitable method to objectively assess the utility of HBM injury predictions, demonstrated with the SAFER HBMs capability to predict rib fracture risk.
Objective: This study investigated injury outcomes and body kinematics in obese occupants exposed to frontal impacts while seated in reclined postures. With increasing interest in non-traditional seating configurations and a growing population of obese vehicle occupants, the objective was to evaluate how seat stiffness and restraint features influence injury patterns and whole-body excursions. Methods: Nine obese post-mortem human surrogates (PMHS; mean age: 64 years, stature: 1.70 m, body mass: 102 kg, BMI: 35 kg/m2) were tested under frontal impact conditions simulating a delta-V of 50 kph. All specimens were seated on a spring-controlled seat with a 45 degrees reclined seatback and restrained by a three-point belt system with pretensioner and load limiter. Three configurations were evaluated: (1) stiffer seat, (2) softer seat, and (3) stiffer seat with a knee bolster 100 mm from the knees. Each subject underwent one test. Whole-body kinematics were captured using a VICON motion analysis system, and injury outcomes were assessed through radiographs, CT imaging, and autopsy, with severity classified by Maximum Abbreviated Injury Scale (MAIS) and Injury Severity Score (ISS). Results: Softer seats produced substantially greater downward (+Z) pelvic displacement compared with stiffer seats. The knee bolster effectively reduced both downward and forward excursions of the lower torso. Rib cage and pelvic injuries were most frequent, with the highest severity observed in softer seat tests. Mean ISS values were: STIFF-27, 13, 27; SOFT-34, 27, 14; STIFF-KB-22, 0, 13. Discussion: Reduced seat stiffness combined with increased occupant mass contributed to greater excursions and anterior torso injuries, whereas the knee bolster mitigated excursion and injury severity. Findings are limited by sample size and test conditions; broader evaluation with production seats is needed to confirm trends and support countermeasure design.
This study provides an updated characterization of real-world frontal crash types-considering overlap and obliquity-based on their overall frequency and associated injury outcomes. The results of this study will support an evaluation of how well NHTSA's frontal oblique crash test condition addresses the current population of serious frontal crashes, as compared to frontal test modes in existing crashworthiness programs. U.S. field crash data from 2017 to 2023 were analyzed to classify frontal crashes by coded damage characteristics. Oblique frontal crashes were defined as those with principal direction of force between 10 degrees-40 degrees and 320 degrees-350 degrees. Non-ejected belted first and second row occupants in model year 2000 and newer passenger vehicles absent a rollover event were included. Occupants were stratified by sex, age, and body mass index, and injury outcomes based on moderate, serious, and fatal thresholds were analyzed across crash configurations. Among the belted first row occupants considered in this study, more than 45% were exposed to oblique crashes while full overlap colinear crashes accounted for 18% of the occupants. Oblique crashes represent a disproportionately large number of AIS 3+ injured and fatal occupants. Older occupants and females showed a trend of higher injury frequency despite less exposure. Full overlap crashes still account for a representative portion of serious injuries among frontal crashes. Limitations include restriction of cases to those with complete vehicle and occupant details. Assessment of impact type was dependent on generic vehicle class-specific reference values. The findings reinforce the enduring relevance of oblique frontal crash conditions which remain a substantial contributor to serious injuries and fatalities, especially for older adults and female occupants.
The aims of this study were to investigate the kinematics of child anthropomorphic test devices in a large sample of rear-facing child restraint system installations and the effects of anti-rebound features and load legs on the kinematics of rear-facing child anthropomorphic test devices. The test matrix included a general sample of 70 rear-facing child restraint system installations to observe trends in frontal crash tests; 14 full-scale crash tests with paired comparisons to investigate the effect of anti-rebound features; and five paired comparisons of rear-facing child restraint systems installed with and without a load leg. The paired t-test was used to determine the statistical significance of differences in kinematic responses. In the general sample, 84% of anthropomorphic test devices in infant seats with the base in outboard seats interacted with the first-row seat. In 52% of tests, the anthropomorphic test device head directly contacted the front seatback. Head accelerations > 80 g were caused by interactions between: the child restraint system and front seatback; the anthropomorphic test device head and the interior surface of the child restraint system; or the anthropomorphic test device head and front seatback. In the anti-rebound sample, head contact on rebound occurred in three infant seat installations, and all were associated with head resultant accelerations <= 33 g. The mean paired difference in head 3 ms clip was negligible (p > 0.05). In the load leg sample, the load leg limited forward excursion and forward rotation of the rearfacing child restraint system, thereby contributing to the containment of the anthropomorphic test device within the boundary of the child restraint system shell. In this study, anti-rebound features did not improve the kinematics of pediatric anthropomorphic test devices. The feasibility of including the use of the load leg in the Canadian regulatory test protocol should be explored.
The objective of this study was to characterize and compare pedestrian automatic emergency braking (PAEB) pulses in modern light vehicles to understand the loading environment that vehicle occupants are being exposed to during PAEB maneuvers. PAEB tests (n = 8008) conducted using 2018-2023 vehicle model years were analyzed. Pulse, vehicle, and impact characteristics (e.g., jerk, peak acceleration, pedestrian scenario, etc.) were derived from each PAEB test. Two k-means clustering analyses were used to group PAEB pulses with and without target collisions based on their similarity between characteristics. One-way ANOVA and Kruskal-Wallis tests were performed on the PAEB pulse characteristics to examine differences between clusters (p < 0.05). Two non-collision clusters (NC1 and NC2) were identified for PAEB pulses without collisions: NC1 had a statistically significant lower jerk (0.8 +/- 0.4 g/s) and peak acceleration (1.0 +/- 0.1 g) compared to NC2 (1.6 +/- 0.8 g/s and 0.9 +/- 0.1 g, respectively, p < 0.001). NC1 was mostly represented by stationary adult (88.6%), 60 km/h (99.5%), and 40 km/h (62.2%) tests. NC2 was mostly represented by crossing scenarios (child: 92.3%; adult: 70.5%) and 20 km/h (96.2%) tests. Three collision clusters (C1, C2, and C3) were identified for PAEB pulses with collisions. C3 showed a greater jerk (1.5 +/- 0.8 g/s) compared to C1 (0.9 +/- 0.6 g/s) and C2 (1.1 +/- 0.9 g/s, p < 0.001). These results suggest that with successful avoidance, deceleration begins earlier with higher speeds and a stationary pedestrian, resulting in potentially milder loading conditions for vehicle occupants (i.e., lower jerk in NC1 vs NC2). With unsuccessful avoidance, in daytime pedestrian crossing scenarios, lower impact speeds were observed, resulting in potentially non-optimal loading conditions (i.e., higher jerk and peak acceleration in C3) for vehicle occupants. At night with low beams, C2 may result in advantageous loading conditions for vehicle occupants (i.e., lower jerk and peak acceleration), but it may lead to the worst outcome for pedestrians (i.e., greatest impact speed).
Objective The objective of this study was to examine the Large Omnidirectional Child (LODC) anthropomorphic test device (ATD) neck and spine responses in reclined seating configurations with and without a backless belt-positioning booster (BPB) in far-side lateral oblique impacts. Methods The LODC was seated on a production passenger seat with an integrated seatbelt and tested in nine lateral oblique impact (80 degrees from frontal) sled tests (31.3 km/h). A condition with a nominal seatback angle (similar to 25 degrees) with a backless BPB and two conditions with reclined seatback angles (similar to 45 degrees and similar to 60 degrees) with and without a BPB were compared. Each condition was repeated, except for the 60 degrees without BPB. Peak upper neck tension force and lateral moment, T1, T6, and T12 lateral rotation, lumbar axial and lateral shear forces, and lumbar axial moment (Mz) were extracted. Results With noBPB, upper neck tension (45 degrees noBPB: 2.0 +/- 0.1 kN; 60 degrees noBPB: 1.8 kN) and lateral moment (45 degrees noBPB: 31.7 +/- 2.3 Nm; 60 degrees noBPB: 29.2 Nm) were greater than with the BPB in all seatback angles (25 degrees BPB: 1.3 +/- 0.04 kN; 21.6 +/- 0.1 Nm; 45 degrees BPB: 1.2 +/- 0.1 kN, 22.5 +/- 2.3 Nm; 60 degrees BPB: 1.2 +/- 0.03 kN, 17.6 +/- 0.7 Nm). Thoracic spine rotation was smaller in reclined conditions with noBPB (41 degrees-59 degrees) than with BPB (63 degrees-80 degrees). Lumbar axial forces decreased with increasing seatback angle with the BPB (from 2.2 to 1.2 kN). Lumbar Mz showed increasing unbelted shoulder rotation toward the seatback with increasing seatback angle (from 29.8 to 37.8 Nm) with the BPB but not without. Discussion The presence of the BPB may improve neck and spine coupled motion during far-side lateral impacts. However, increased lumbar Mz with the BPB in recline seatbacks requires further understanding.
Aims of the research This study aims to modify the lower body (the pelvis, thigh, and leg) of the mid-sized male pedestrian dummy FE model by considering the latest version of the physical dummy and to evaluate both the accuracy by comparing test results of the past studies and the biofidelity specified in SAE J2782 in both component and full-scale validations. Methods 1. Component validation The validation of the modified pelvis model was performed in dynamic lateral compression simulations. The sacrum and the pubis force-deflection responses of the iliac or the acetabulum impact were measured. The modified thigh and leg models were evaluated in a dynamic 3-point lateral bending simulation, measuring the force-deflection responses. The results from the simulations were compared with test results and the biofidelity requirements. 2. Full-scale validation The whole-body model was updated by incorporating these modified component models. The model of the generic buck developed for the assessment of pedestrian whole-body impact response and specified in SAE J3093 was used in this study. The buck model was made to collide with the full-scale dummy model at 40 km/h laterally. The trajectories of the head, upper spine, mid-thorax, and pelvis were measured and compared with those of the test results and the biofidelity requirements. Results The force-deflection responses from the pelvis, thigh, and leg models were similar to those of the test results, indicating they almost fell within the biofidelity requirements. As the results of the full-scale simulation, the trajectories of the head, upper spine, mid-thorax, and pelvis showed a strong agreement with those of the test results, indicating almost the same tendency as the biofidelity corridors, except for that of the pelvis. Conclusions As the results of component and full-scale validations, the equivalences of the modified pedestrian dummy model to test results and the biofidelity were confirmed in most cases.
This study investigated how vehicle front-end geometry, impact speed, and vehicle category influence injury risk to a midsize male pedestrian. Eighty-one generic vehicle (GV) models representing sedans, sport utility vehicles (SUVs), pickup trucks, and minivans sold in the United States were developed by morphing three base models using an automated pipeline. Front-end parameters that were varied included ground clearance (GC), bumper height (BH), hood leading-edge (HLE) height, hood length (HL), bumper lead angle (BLA), hood angle (HA), and windshield angle (WSA). Each vehicle impacted the Global Human Body Models Consortium 50th percentile male simplified pedestrian (GHBMC M50-PS) model at 30, 40, and 50 kph, totaling 243 simulations. Boundary conditions followed the European New Car Assessment Program (Euro NCAP) pedestrian test protocol. Thirty-five injury metrics were extracted across the head, neck, thorax, abdomen, pelvis, and lower extremities. Linear mixed-effects regression models assessed relationships between vehicle front-end geometry, impact speed, and injury outcomes, with predictor selection guided by principal component analysis (PCA) and collinearity diagnostics. Impact speed was the strongest predictor of injury severity across all body regions. GC and HLE height were also dominant predictors. Wrap-type trajectories were common at lower speeds and in SUVs, trucks, and minivans, while sedans and minivans showed roof vaulting at higher speeds. Head injury severity increased with speed and was influenced by HA and BLA. Minivans showed elevated brain injury criterion (BrIC) and cumulative strain damage measure (CSDM25) values, indicating increased diffuse brain injury risk. Trucks produced the highest thoracoabdominal injury metrics, which correlated with HL, HA, femur forces than SUVs and minivans, and lowest tibia moments. Trucks had greater tibia bending moments, while SUVs and minivans had higher left femur moments compared to sedans. GC and impact speed exacerbated lower extremity injuries, varying by vehicle category. These effects are driven by geometry: Higher GC increases the unsupported span below the knee, promoting tibial bending, while lower HLE heights shift impact forces above the knee, elevating femur injury risk.
Research Question/Methods The study examined abdominal injuries of 87 belted occupants in CIREN frontal crashes for sex-based differences in abdominal injury patterns. It introduced a more anatomically detailed method for identifying injury locations in an abdominal-pelvic region that includes skeletal structures. The study introduces and applies a novel Abdominal New Injury Severity Score (AbNISS) to address limitations of traditional AIS coding in capturing sex-based differences in injury patterns. The operative reports/EDR/imaging data in CIREN cases enabled identification of sex-specific crash outcomes. The dominant analytical motif is Betrand Russel's knowledge by acquaintance and definite descriptions. Results Females had a higher rate of moderate to severe abdominal injuries than males: Only females sustained AIS 5 injuries, lumbar Chance fractures, posterior pelvic arch injuries, and more AIS 2, 3, and 4 injuries, with more injuries in superior-mid, left-superior, and medial-mid-abdominal zones. Males had more in the medial-inferior zone. 13 females had muscle ruptures. Four had combined muscle and Morel-Lavall & eacute;e injuries, and four had Morel-Lavall & eacute;e injuries alone. 13 of 14 males had muscle ruptures only. Pelvic morphology: Statistically significant (p < 0.05) sex-based difference in pelvic aspect ratios: Females: 1.37 +/- 0.053, with the male ratio of 1.28 +/- 0.079. By incorporating anatomical precision and enhanced injury mapping, it supports the development of more representative ATD/human body models. Discussion and Limitations Limitations of the study include its retrospective design, possible inconsistencies in clinical documentation, and challenges in applying AbNISS coding to non-CIREN datasets due to specificity constraints.
NHTSA is conducting research to evaluate the current state-of-the-art technology for lane departure warning (LDW) and lane-keeping assistance (LKA) technology. NHTSA is undertaking research to understand the nature of real-world lane departures and recovery behaviors. While some information about lane departures can be learned from crash datasets, the purpose of this work was to mine simulator datasets for lane departures, analyze them in greater detail than is possible from crash reports or naturalistic studies, and link their characteristics to driver drowsiness. The objective of the study was to determine whether there are differences in lane departure characteristics as a function of driver drowsiness. This research used a novel approach by combining data from six different driving simulator studies on driver drowsiness. The dataset included a sample of 380 drivers. Study drives occurred during overnight hours after periods of sleep deprivation, with participants being awake for at least 16 h prior to driving. Study drives ranged in duration from relatively short 45-min to nearly 4 h. The datasets were reduced to characterize 5805 individual lane departures. Lane departures were delineated into three phases (pre-departure, departure, and recovery) and two transition points (onset and reentry) to capture driver behaviors under drowsiness. We hypothesized that lane departures would look different under different levels of drowsiness. Drives took place across a range of roadway environments that included interstate highways, rural highways, rural roads, and low-speed urban areas. Drowsiness was sampled at points before, during, and after the drive using self-ratings [Karolinska Sleepiness Scale (KSS) or Stanford Sleepiness Scale (SSS)] as well as the expert Observational Rating of Drowsiness (ORD). High levels of drowsiness were associated with a narrow speed range at highway speeds and the least amount of throttle input, while low levels of drowsiness had more steering activity, more throttle input, and a broader range of speeds. The results of this study will improve understanding of vehicle kinematics and driver behavior in drowsy lane departures using a safe methodology to help address crash dataset limitations.
Drivers frequently encounter Type II dilemma zones at signalized intersections, where the decision to stop or proceed during the onset of a yellow indication can be ambiguous. Decision-making relies on drivers' expectations of the yellow change interval duration and behavioral factors. While boundaries of these zones are well studied, less is known about how familiar drivers are with their local yellow indication laws, which vary from state to state, and whether their typical reactions to yellow indications align with the laws. Existing interventions like signal timing adjustments, improved vehicle detection, and advance warning signs reduce the number of drivers caught in dilemma zones but may not reach distracted drivers. In-vehicle alerts tailored to dilemma zone scenarios are a potential solution not yet implemented widely in North America. This study addresses how drivers may interpret these alerts. A web-based survey of 640 licensed drivers in Michigan and Washington (ages 18-85) assessed respondents' knowledge of their state's law, typical responses to yellow indications, interpretations of proposed in-vehicle alerts, and preferences for alert modality, frequency, and placement. These states were selected for their differing yellow indication laws-restrictive in Michigan, permissive in Washington. Nine alert visuals were tested, including pairs of implicit and explicit messages, and were inspired by or designed to address gaps in prior research. Respondents evaluated these alerts in response to hypothetical intersection scenarios that varied by the presence of other vehicles. Results revealed a prevalent misunderstanding of local yellow indication laws across both states. Statistical analyses showed significant differences in rankings among the nine alert visuals, and explicit messages showed higher rates of correct interpretation. Findings show overall driver support for dilemma zone alerts, but higher receptivity in drivers who more frequently use other ADAS features and lower receptivity in drivers within older, but not the oldest, age groups. Future research could explore whether these alerts promote safe behaviors aimed at crash avoidance.
Previous rear-facing post-mortem human subject (PMHS) studies utilizing a reinforced seat have prompted questions as to whether the seat could have been a contributing factor to the severe rib and pelvis injuries observed in those experiments. In response, a recent PMHS study used an unreinforced seat in a similar experiment, which was expected to mitigate severe injuries by dissipating energy from seatback deformations. However, the PMHS tested in the unreinforced seat sustained even more severe rib fracture numbers than in the reinforced seat. No studies have investigated how additional variables (i.e., countermeasures) may influence rib fractures in high-speed rear-facing frontal impacts (HSRFFI). Therefore, this study aimed to explore the effect of an airbag-equipped seat (AES) on male PMHS responses and injuries. Rear-facing sled tests were conducted using five mid-size male PMHS seated in the AES at Delta V of 56 km/h: PMHS1 with no airbag as a baseline, PMHS2 with a seatback airbag (SA), PMHS3 with an extended seatback airbag (ESA), and PMHS4 and 5 with ESA and a wedge airbag (ESA+WA). An instrument panel (IP) and windshield were installed behind the seat to mimic realistic interior vehicle compartments. A chestband at mid-sternum, 6-degree motion blocks at the head, T1, T4, T8, T12, pelvis, and extremities, as well as rib strain gages and rosettes were installed on PMHS to understand potential mechanisms of injuries. A motion capture system was used to quantify wholebody PMHS and seatback kinematics. Maximum seatback rotation was 38.1 degrees in the baseline test and 20.3 degrees-25.1 degrees with AES. Peak chest A-P compression in the anterior-posterior (A-P) direction was 25.7 mm for baseline and 7.3 mm-35.2 mm with AES (23.7 mm for SA, 7.3 mm for ESA, 35.2 and 8.7 mm for ESA+WA). The number of rib fractures (NRF) was high in baseline (32), SA (25), and ESA (27) conditions, but was reduced in ESA+WA (6 and 13). Strain rosette data indicated upward directions of principal strains on the posterior ribs, likely due to I-S deformation of the PMHS thoraces. Responses from thorax instrumentation showed that peak chest deflection (A-P) alone did not fully explain NRF, especially as rib fractures in all tests occurred after peak deflection in this direction. Instead, maximum principal strains in the I-S direction (shear), confirmed by strain rosette data, likely influenced rib fractures. ESA+WA effectively supported PMHS, maintaining upright postures and minimizing and seat designs intended for low-speed rear impacts, not HSRFFI. Compression and shear loading to the PMHS thoraces were observed in HSRFFI. The shear loading was likely due to the large upward thorax deflection induced by the ramping motion and seatback rotation. One of the AES, ESA+WA, effectively maintained and designing rear-facing countermeasures for automated driving systems.
Vehicles equipped with an Automated Driving System (ADS) have the potential to significantly reduce road collisions. To enable widespread adoption of ADSs, rigorous safety assessment is essential. Valuable insights for ADS safety validation can be gained by simulating scenarios across a broad range of feature variations. A common challenge in simulating these scenarios is known as the curse of dimensionality, where increasing the number of scenario features requires a near-infinite number of simulations to cover all variations. This issue of complexity presents a need for reducing scenario features. Most related work focuses on identifying important scenario features, while few evaluate how reducing these features impacts ADS failure estimation. The present study aims to address this gap by employing a wide range of feature reduction methods and assessing their effect on ADS failure estimation. Previous research generated datasets for three distinct scenario categories by performing virtual simulations using driver reference models on real-world data. In the present work, the machine learning classifiers such as extreme gradient boosting and random forest are applied to this data for predicting ADS failures. Ten dimensionality reduction techniques, including both feature selection and transformation approaches, are employed to reduce the scenario feature set. The optimal reduced feature set is selected based on classification performance measured by the area under the precision-recall curve. To assess the impact on ADS failure estimation, results are compared against those obtained with the full set of features. The findings indicate that reliable ADS failure estimates can be maintained, and even significantly improved, after substantially reducing the number of scenario features. By reducing scenario features, fewer virtual simulations may be required to reliably estimate ADS failures, which may enable more efficient scenario-based ADS safety assessment. Additionally, this study may offer guidance on selecting suitable dimensionality reduction techniques for scenario-based ADS safety assessment.
Automated Vehicles (AV) pose new challenges in road safety, multimodal interaction, and urban planning, requiring a holistic approach that prioritizes sustainability and protects all road users. The KASSA.AST project addresses this by deploying and evaluating an automated shuttle in southern Austria on three routes. The study area is a Park & Ride zone near a train station, enabling seamless transfers and higher transit use. To assess the safety impacts of the automated shuttle, four Mobility Observation Boxes (MOBs) were deployed. These AI-based systems detect and classify road users, track their trajectories and geospatial coordinates, and identify safety-critical events via Surrogate Safety Measures (SSMs). Over 10 days, a trajectory dataset captured interactions among vehicles and the shuttle. The resulting real-world dataset is a core contribution. This dataset underpins microscopic behavior modeling. Trajectory pairs yield car-following and interaction metrics (relative distance, relative speed, acceleration) to calibrate custom models for realistic mixed traffic. Simulations generate a structured interaction database with time spans, trajectories, conflict points, and SSMs crash-DRAC). These outputs support detailed analysis of shuttle interactions, including near misses. To reveal patterns, clustering identified three interpretable safety-relevant regimes: (i) a low-demand background regime (n = 96) with low speeds and near-zero deceleration demand, (ii) a fast-and-tight regime (n = 33) with reduced TTC, elevated critical-event speeds, and high DRAC/Modified (M)DRAC demand, and (iii) an AV-regulated regime (n = 10) dominated by the shuttle as adversary, showing short TTC but stable moderate speeds (similar to 4 m/s) and conservative headway policies. Ensemble-tree supervised learning reproduced these regimes with high accuracy and revealed that critical-event speeds and counterpart headway are the strongest discriminators, while AV role metadata contributes marginally. This integrated approach-linking field data, behavior modeling, simulation, and machine learning-provides a robust framework for assessing AV safety in urban contexts.
Programs that teach older drivers how to confidently and competently use advanced vehicle technologies (AVTs) are limited. The MOVETech study evaluated a training program specifically designed to teach older drivers how to use these technologies. Participants (n = 119) were randomized to the intervention (training program) or control group (brochure). The intervention involved an in-person classroom education session on the use and benefits of AVTs, and an on-road driving session where participants drove along a pre-defined route in a dual-controlled vehicle with instruction on AVT use by a driving instructor. All participants completed in-person and telephone assessments at baseline and 3 months. Driving performance and on-road AVT competence assessments were the primary outcomes. Self-reported driving confidence, competence, and confidence in use of AVT, crashes, citations, and count of vehicle damage were the secondary outcomes. Program fidelity was also evaluated using a checklist. At 3 months, overall driving performance was high (96/100) and similar between groups. The intervention group, however, had slightly higher competence in AVT use (77 vs 73), but the between-group difference was not statistically significant (4.14, 95% CI -4.85 to 13.13). There were no differences in secondary outcomes. Program fidelity was high for all classroom sessions but varied for on-road sessions due to external and environmental factors, which impacted how AVT was demonstrated. The findings indicate AVT competence and confidence may be improved by combining classroom and on-road sessions, and importantly, that this type of program is feasible and very well-accepted among older drivers. Future work could target drivers with new vehicles who are unfamiliar with AVT to determine potential real-world benefits. This study provides evidence for vehicle manufacturers and policymakers to explore efficient ways of providing support to older drivers with AVT.
Objective: This study sought to implement pressure mapping methodology to assess variation in children's center of force positions in reclined vehicle scenarios. Methods: Thirty-four children between 4 and 12 y (8.1 +/- 2.0 y) were statically evaluated on a vehicle seat across two seating conditions (with and without a backless booster) and three seatback recline conditions (25 degrees, 45 degrees, and 60 degrees). Center of force was recorded using pressure sensors attached to the seating surface. Average center of force fore/aft positions were calculated and transformed into the vehicle coordinate system using 3D coordinate measurements. Descriptive statistics and repeated measures ANOVA were used to assess variation in center of force position across seating and recline conditions, with subject included as a random effect. Results: Center of force fore/aft position varied (p < 0.05) with recline condition, seating condition, and the recline/seating condition interaction term. On the booster, the average center of force position became more aft in the 45 degrees (131.1 +/- 17.5 mm) and 60 degrees (125.5 +/- 16.7 mm) conditions compared to 25 degrees (148.7 +/- 17.4 mm). Without the booster, the center of force also became more aft in the 45 degrees (197.7 +/- 31.1 mm) condition compared to the 25 degrees condition (204.6 +/- 29.1 mm), but the position in the 60 degrees (206.1 +/- 31.8 mm) condition was similar. As children assumed more reclined postures, the center of force became more aft, except for the no-booster 60 degrees condition. Discussion: Changes in center of force followed the same trends observed in the pelvis and lower extremity position (became more aft) with increasing seatback recline on the booster and smaller changes observed on the no-booster condition. Future work should investigate additional vehicle/booster geometries and longer seating durations. The changes in center of force observed with seatback recline emphasize the importance of understanding how real children modify their posture over time to different vehicle environments as posture directly influences belt fit, occupant-restraint interaction, and injury risk. Center of force data can inform the positioning of child surrogates in future dynamic evaluations of reclined configurations.
Vehicle maneuver data are essential for perception and planning in advanced driver-assistance systems (ADAS) and automated driving systems (ADS). While high-quality annotations improve machine-learning performance, existing maneuver datasets remain fragmented, labor-intensive to annotate, and inconsistent in semantic richness. Challenges persist in scalability, interpretability, and contextual labeling. This article establishes a structured framework for maneuver data analysis by combining a systematic review of existing resources with the development of a new multimodal dataset. First, we conduct a systematic review of publicly available datasets such as HDD, KITTI, BDD-X, D2CAV, Brain4Cars, DrivingDojo, and the Driving Behavior Database. We further evaluate the data modality and sensor configurations including event data recorders, onboard logging systems, and smartphone sensing. We then propose the Matt3r Data Collection System with modern metadata management, which integrates video, GPS, and IMU signals into temporally coherent clips. Next, we outline the limitations of traditional annotation approaches, which rely on manual labeling and rule-based methods. To address the limitations of traditional manual and semi-automated labeling, we propose a Vision-Language Model (VLM)-driven annotation pipeline. VLMs generate maneuver categories and causal explanations through prompt-based reasoning, with selected outputs refined through human-in-the-loop verification. Finally, we propose an annotation quality evaluation based on accuracy, inter-annotator agreement, credibility, consistency, and efficiency gain. In summary, this article bridges the gap between the environment perception requirements of existing ADAS and ADS systems and the developing capabilities of generative artificial intelligence. By providing a novel and scalable research approach for AI-driven maneuver data annotation and analysis, this article supports data engineering efforts for both research and practical applications aimed at enhancing vehicle safety.
To reduce traffic fatalities through vehicle safety measures, particular attention must be given to cyclist-related fatalities. Clarifying the characteristics of hazardous events leading to cyclist fatalities, not only byvehicle speed range but also byvehicle type, is essential and should be based on analyses of real-world accident data. Accordingly, this study aimed to characterize fatal cyclist accidents involving vehicles traveling at low and high speeds in Japan. We used macro accident data from the Japanese Institute for Traffic Accident Research and Data Analysis covering the period from 2013 to 2022. Based on nine vehicle types, we investigated the effects of road type, vehicle behavior, and accident type on cyclist fatalities. Additionally, we identified the five most frequent accident scenarios separately for each low-and high-speed category. At signalized intersections, the proportions of cyclist fatalities involving vehicles traveling at low speeds were higher than those involving vehicles traveling at high speeds across all vehicle types. In contrast, on straight roads, the proportions at low speeds were lower than those at high speeds for all vehicle types. In the low-speed range, cyclist fatalities within the top five scenarios accounted for 65% of all fatalities, with the most frequent scenario occurring at signalized intersections during left-turn maneuvers, where heavyduty trucks accounted for 86% of the fatalities. In the high-speed range, cyclist fatalities within the top five scenarios accounted for 71% of all fatalities. The most frequent high-speed scenario involved crossing collisions at unsignalized intersections when vehicles traveled straight, with light passenger cars and sedans accounting for 29% and 24% of the fatalities, respectively. These findings provide valuable insights for the development of targeted traffic safety regulations and vehicle technologies aimed at reducing vehicle-cyclist collisions across different speed ranges.
