Crash Mitigation Research Articles

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Tunable and recoverable energy absorption of foam-embedded architected cellular composite material at multiple strain rates

Cellular multistable architected material has been widely studied for its promising energy absorption applications for personnel protection, protective packaging, and crash mitigation. Despite the tailorable properties enabled by their geometric features, a major issue in the actual application is their lower strengths in the elastic behavior of the multistable architected material. Herein, a novel design strategy of strain rate-dependent architected cellular composite material (FACCM) is proposed, aiming to achieve enhanced energy-absorbing properties with the proposed composite system than the single material constituent. Guided by numerical simulations and experimental tests, the compressive behaviour of FACCM at multiple strain rates and the effect of filled foam on it with specific geometric parameters are characterized. The quasi-static performances of the FACCM specimens were evaluated both at the cell level and planar array level. Our results indicate that the strength, stiffness, and snap-through behaviour of multistable architected material can be significantly improved by the filled foam at elevated loading rates regardless of quasi-static loading or impact tests. The current study offers a new strategy for developing novel packing, shock absorption, and impact protection systems.

Secondary crash mitigation controller after rear-end collisions using reinforcement learning

Rear-end collisions result in a large number of casualties and property losses, and the serious injury risk in multiple impact accidents is much higher than that in single impact accidents. In this paper, we propose a novel controller to facilitate the prevention of secondary crashes after an initial rear-end collision, which expands the operational horizon of conventional vehicle active safety systems from preventive measures to post-event mitigation measures. Considering the complexity of the problem with multi-object synthesis optimization and vehicle nonlinear dynamics, this study combines the pre-collision control and post-collision control to reduce the initial crash loss and the subsequent control difficulty. The rule-based switching control and drift manipulation are embedded into the reinforcement learning algorithm to improve the training efficiency and control performance. The bench test results validate the superiority of the proposed controller over other strategies and algorithms in different rear-end collision scenarios.

Assessment of Deer–Vehicle Crash Mitigation Structures on the US 33 Nelsonville, Ohio, Bypass

The US 33 Nelsonville Bypass project involved construction of a new four-lane divided highway through the Wayne National Forest in southeastern Ohio, an area high in species diversity and home to several threatened and endangered species. The project included three wildlife underpass structures to promote habitat connectivity, 16 jump out structures to allow wildlife to exit the highway right-of-way (ROW), and 2.44-m (8-feet) tall exclusion fencing along the ROW boundary to discourage entry. This paper reports the results of a comprehensive research study undertaken to determine if these features were effective at reducing collisions between vehicles and wildlife (specifically, white-tailed deer). A multi-season camera-based monitoring plan was deployed to track the movement of animals within and around the highway ROW. The results of the analysis showed that the crossing structures were heavily used and that the tall fencing was effective at excluding deer from the highway area. Approximately 43% of deer that approached the jump out structures used them to successfully exit the ROW. Analysis of traffic crash data from six years after the opening of the highway suggests that the prevalence of deer crashes on the highway is lower than other highways in the region, indicating that the mitigation features appear to be successful at reducing such collisions. Considering the additional costs of constructing the mitigation features and the economic benefits of the associated deer crash reductions, the incremental cost-benefit ratio of the complete installation is estimated to be 1.65, indicating a net positive benefit realized by the mitigation plan. The results of this study suggest that wildlife mitigation structures can be effective in supporting highway development in wildlife-sensitive areas, particularly if the placement of the structures relates to existing wildlife patterns and the features are maintained post-construction.

ASSESSMENT OF AUTONOMOUS EMERGENCY BRAKING (AEB) PEDESTRIAN SYSTEM IMPACT ON 2016 – 2020 MALAYSIAN ANIMAL-CROSSINGS ACCIDENTS DATA

The trend of animal-vehicle collision (AVCs) occurrences over the past years demonstrates increasing numbers, and this call for a proper mitigation plan by appropriate authority bodies. Autonomous Emergency Braking (AEB) pedestrian system - proven effective in collision prevention and mitigation for vehicle-pedestrian collision – can potentially expand its original functionality to AVCs avoidance. This study presents a new data assessment method to predict the impact of AEB pedestrian system implementation on vehicles to reduce AVCs cases from 2016 to 2020. In general, a new scoring system is introduced whereby fitment rating points of 1, 0.5 and 0 are given to describe successful crash avoidance, crash mitigation with reduced damage and failed crash avoidance. Several noteworthy findings were discovered in assessing impact data from five significant AEB-AVCs. The effectiveness of AEB is found to be correlated with camera detection, system working speed range, frequent collision time, human casualties, and heavy vehicles. In general, the results indicate overall positive consequences of AEB implementation to reduce AVCs, providing concrete reasoning for standardising AEB pedestrian systems in all manufactured road-legal vehicles for upcoming years.

Multifunctional architected MWCNTs/PDMS composites with high sensing and energy absorption capability inspired by ant tentacle and pomelo peel

The multifunctional features of pressure sensitivity and energy-absorption capability of flexible electromechanical sensors are desirable for practical applications, such as personal protection, crash mitigation, and protective packaging of sensitive elements. However, there are still challenges in developing ultrasensitive pressure sensors with high energy absorption capability. Herein, a bio-inspired flexible piezoresistive MWCNTs/PDMS composite with a multilayered architecture mimicking the ant tentacle and the pomelo peel is innovatively constructed, which can simultaneously detect physical stimuli in an extensive pressure range and absorb impact energy. The composite architecture is composed of the bottom-layer interlocked tentacle-like conical micropillars and the top-layer pomelo peel-inspired hierarchically enclosed-porous microstructure, which has successfully wrapped air inside to form a solid-gas dual phase structure with enhanced energy absorbing capability and piezoresistivity. The composite architecture was manufactured by a laser-engraving method associated with the controlled solidification process of the composite by regulating the vacuum pumping rate in the pre-curing condition. Owing to the hollow-shaped tentacle-like conical micropillars, the contact area can be dramatically increased within a subtle pressure loading, leading to superb pressure sensitivity of ∼26.1 kPa−1. Simultaneously, the hierarchically enclosed-porous structure with trapped air (gas-phase), acting as an air cushion packaging, imitating the pomelo peel provides the composite architecture with exceptional mechanical energy absorption of ∼2.74 MJ/m3 and an extensive pressure sensing range from 0.1 Pa to 60 kPa. As a result, the as-proposed composite sensor was able to detect mechanical signals such human finger tapping, wrist flexion, and pendulum hammer impact. Numerical simulations also showed that the wrapped air inside the hierarchical enclosed-porous structure improved the mechanical damping properties of the composite architecture. It is envisioned that our finding could contribute to the development of multifunctional materials to meet the growing demands for next-generation electronic systems, inspired by sophisticated architectures from nature.

Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: Association knowledge using data mining approach

Introduction: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. Data: Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with and without streetlight). Method: This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized. Results and conclusions: Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight condition, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In dark lighting condition (with and without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15–24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep), and colliding with animal(s). Practical Applications: The findings also reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.

Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collision (UC), especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation (CM) algorithm for UCs, which directly embeds a generalized crash severity index (CSI) model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized CSI model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severity-optimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of UC scenarios, including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly used collision avoidance (CA) strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on CA, but also the last resort of CM if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

Changing vehicles to reduce older driver fatalities: An effective approach?

Introduction: Age-related frailty leaves older drivers with the greatest fatality risk when involved in a crash compared with younger demographics. This study explored how vehicle features differed between crash-involved older and middle-aged drivers and estimated how those differences contribute to excess older driver fatalities. Methods: We merged Florida's crash data from 2014–2018 with Insurance Institute for Highway Safety and Highway Loss Data Institute databases. We compared the distribution of passenger vehicle age, type, size, and safety features among crash-involved older drivers (ages 70 and older) with crash-involved middle-aged drivers (ages 35–54). From logistic regression models, we estimated declines in older driver fatalities if they drove vehicles like those driven by middle-aged drivers under all and side-impact crash scenarios. Results: Older drivers in crashes were more likely to be in vehicles that were lighter, older, and without standard electronic stability control, standard head-protecting side airbags, and ratings of good in two IIHS crash tests than middle-aged drivers. In adjusted models, the fatality risk for older drivers in all crashes was significantly higher when ESC was not standard (odds ratio [OR], 1.37; 95% confidence interval [CI], 1.12–1.68) or when driving small passenger cars relative to large SUVs (OR, 2.02; 95% CI, 1.25–3.26); in driver-side crashes, the fatality risk doubled when vehicles did not have standard head-protecting side airbags (OR, 2.03; 95% CI, 1.58–2.62). If older drivers drove vehicles similar to middle-aged drivers, we estimated 3.3% and 4.7% fewer deaths in all and side-impact crashes, respectively. Conclusions: These results contribute to evidence suggesting that newer, more crashworthy vehicles with crash mitigation features benefit older drivers because of their heightened risk of crash-related fatality. Practical Applications: At a minimum, older drivers should aim to drive equipped vehicles with widely available features proven to reduce fatalities.

Efficient crash structure design for road traffic accidents of tomorrow

Energy absorbing structures (EAS) play a vital role in automotive crash mitigation systems and save countless lives each year. Innovative EAS design ideas such as cross-sectional geometry is an area under intense research as evidenced by a large number of publications in recent years. It is however very difficult to directly compare the efficiency of the design suggestions due to parameter variation. This paper identifies designs with the highest potential, introduces an efficient reduced order modelling methodology and provides the first comprehensive unbiased benchmark. Critical evaluation of results reveal that some designs have inherent issues not previously reported, whilst others demonstrate significant potential for improving EAS performance compared to “conventional” designs.

Crash mitigation controller for unavoidable T-bone collisions using reinforcement learning

T-bone collision constitutes an emergency crash scenario that results in casualties and heavy losses; it is an excessively complicated scenario that cannot be handled by conventional control systems. This paper presents an innovative crash mitigation controller for application during unavoidable T-bone collisions to expand the vehicle-maneuverability envelope and minimize crash severity; this controller combines prior knowledge using an optimum expert-behavior policy and drift-operation mechanism based on an improved reinforcement learning algorithm, TD3. Vehicle and tire modeling are performed considering the nonlinear and coupled dynamics characteristics to improve control accuracy. Unlike conventional control systems and other reinforcement learning algorithms, the proposed controller realizes the optimum crash mitigation effect under different scenarios. It is expected to afford autonomous driving technologies with enhanced operating capabilities under extreme conditions.

3D interpenetrating piezoceramic-polymer composites with high damping and piezoelectricity for impact energy-absorbing and perception

Materials and structures with enhanced energy-absorbing and impact perception capabilities are widely deployed for crash mitigation, protective packaging of sensitive elements, and personal impact protection. However, conventional lightweight structures with a monolithic constitutive material cannot simultaneously achieve exceptional energy-absorbing capacity and electromechanical sensitivity. Here we proposed a new class of architected ceramic-polymer composites with improved energy-absorbing capacity and piezoelectric performance. An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48 )O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. Subsequently, the polymeric polydimethylsiloxane (PDMS) was impregnated into the skeleton to form the three-dimensional (3-D) interpenetrating-phase piezoelectric composite (IP 3 C). The as-fabricated interpenetrating architecture with each phase interconnected has endowed the proposed IP 3 C with an unprecedented combination of mechanical-damping (energy-absorption efficiency ∼ 7.71 MJ m −3 ) and electromechanical-conversion (piezoelectric constant d 33 ∼ 146 pC N −1 ) properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. As evidenced by numerical simulations, this remarkable enhancement is attributed to the high stress transfer efficiency within the IP 3 C, which is intrinsically controlled by the rationally designed interpenetrating architecture. The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments. • An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48 )O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. • The as-fabricated interpenetrating architecture with each phase is interconnected has endowed the proposed IP 3 C with an unprecedented combination of mechanical-damping and electromechanical-conversion properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. • The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments.

Spatial predictions of harsh driving events using statistical and machine learning methods

Harsh driving behavior events, such as harsh braking events (HBs) are road safety surrogate measures showing promising research venues towards crash mitigation, such as safety evaluations based on high-resolution driving data from smartphone sensors. This research presents a framework for aggregation and modelling of such data to highlight safety critical locations based on geometric and network characteristics. Spatial models including Geographically Weighted Poisson Regression, Bayesian Conditional autoregressive models (CAR), and variations of EXtreme Gradient Boosting (XGBoost) are implemented. The purpose is to: (i) explore parameters affecting frequencies of harsh driving events through causal spatial models in an urban road network and (ii) assess the predictive performance of models by testing the transferable components of these models in a new urban network test area. The models are trained and evaluated in terms of accuracy and transferability for HBs predictions in separate areas of Athens, Greece. Findings indicate that geometrical characteristics affect HB frequencies per road segment: Segment length and adjusted pass count are positively correlated with HBs, while gradient and neighborhood complexity are negatively correlated with HBs. Lane number and road type have more unclear and circumstantial effects overall. Two-lane segments have statistically higher HB frequencies compared to one-lane segments, while residential type segments have statistically lower HB frequencies compared to primary road segments. Furthermore, successful spatial predictions were conducted by averaging the results of all four methods, achieving accuracy of more than 87% for HB frequencies per road segment. Finally, the implications towards proactive traffic safety management and the extension possibilities for other harsh event types are also discussed.

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

Driver Response Time in Cut-Off Scenarios from the Second Strategic Highway Research Program Naturalistic Database

A cut-in or cut-off scenario involves a vehicle intruding into the path of another vehicle traveling in the same direction. These lane changes can lead to potentially dangerous situations, either a sideswipe or a rear-end crash. In this study, 552 cut-in events were analyzed, including four crash and 548 near-crash events from the Second Strategic Highway Research Program (SHRP-2) data set. Video and onboard-data-recorder data from the responding vehicle were used to analyze various factors associated with drivers’ responses. Driver response times were measured from three different event onsets, and the effects of different factors on the respective response times were measured. These factors included the behavior of the subject driver, the behavior of the intruding vehicle/principal other vehicle (POV), and different environmental and infrastructural factors. The results showed that drivers responded more slowly when the POV took longer to move laterally to the subject driver’s lane edge and faster when this time was short. Similarly, drivers responded faster to merging vehicles that started from a stop. Yet, response times were no different when the POV utilized a directional signal. These results point to a kinematic threshold involving lateral distance and lateral speed that best describes how drivers were triggered to respond. Drivers also responded faster near intersections, and at night. The results can be utilized to design crash mitigation systems in autonomous vehicles, as well as non-automated vehicles, to supplement human responses where their abilities may be lacking.

Field effectiveness of general motors advanced driver assistance and headlighting systems

This study examined the field effectiveness of General Motors advanced driver assistance and headlighting systems. A total of 8,311,707 Model Year 2013–2019 vehicles were matched to police-reported crashes from 12 states. The quasi-induced exposure method was used to compare system-relevant and system-irrelevant (control) crash counts for equipped and unequipped vehicles. Logistic regression was used to adjust for ten covariates. Results indicated fusion/radar Automatic Emergency Braking, camera Automatic Emergency Braking, and Forward Collision Alert systems reduced rear-end striking crashes by 45%, 38%, and 20%, respectively. When restricting data to crashes in which someone in the General Motors striking vehicle was injured, these reductions were elevated to 59%, 54%, and 31%, respectively, providing evidence of additional crash mitigation benefits. Similarly, the Lane Keep Assist with Lane Departure Warning and Lane Departure Warning (alone) systems provided 12% and 10% reductions in lane departure crashes, respectively, with corresponding benefits in the injury analysis increasing to 19% and 18%, respectively. The Lane Change Alert with Side Blind Zone Alert system reduced lane change crashes by 16%. Reverse Automatic Braking, Rear Cross Traffic Alert, Rear Park Assist, and Rear Vision Camera (where each of these systems generally included all of the preceding systems) produced, respectively, an 82%, 55%, 36%, and 24% reduction in backing crashes. For Front Pedestrian Braking, a non-significant 14% reduction was observed for the limited set of available pedestrian crash cases. Intellibeam (auto high beam headlighting), High-Intensity Discharge headlights, and the combination of these two systems provided 26%, 11%, and 32% reductions (relative to halogen headlights) in a combined set of (unlighted) nighttime animal, pedestrian, and bicyclist crashes, respectively. These results provide widespread evidence of the substantial crash avoidance and injury reduction opportunities afforded by the production systems evaluated, as well as identify untapped system opportunities for moving toward a zero crashes vision.

Long-term effectiveness of reverse linear perspective markings on crash mitigation in car-following: Evidence from naturalistic observations

Perceptual markings on roads are verified with short-term effectiveness for accident prevention. However, the long-term performance of them is seldomly investigated, which unintentionally impedes its more widely recognition and application as a low-cost and readily achievable countermeasure. Also, the previous perceptual markings were only tested for speed reduction effect, little is known concerning their influence on headway adjustment. Given this, in this study, we investigated the short-, medium-, and long-term performance of the reverse linear perspective markings (RLPMs) on driving behaviors and safety benefits in car-following. The RLPMs were a form of markings pattern that can produce reverse linear perspective visual information on the lane and lead to distance underestimation. The RLPMs were permanently installed on a straight and a curve segment of a freeway in China, and the naturalistic vehicle flow data one day, four months, one year, two years, and three years after the installation of the RLPMs were collected. The statistical analyses of general and sectional relative differences of speed, distance headway and time headway suggest that 1) the speed reduced and distance and time headways increased in short-, medium-, and long-term as compared with the baseline on both the straight and curve segments; 2) the long-term performance of RLPMs significantly weakened as compared with the short-term performance, yet sustained to 0.50 m/s in speed reduction, 3.77 m in distance headway increase, and 0.097 s in time headway increase on average within the observations in one year and above on the straight segment; similar sustained performance of 0.47 m/s in speed reduction, 2.60 m in distance headway increase, and 0.072 s in time headway increase were observed on the curve segment; 3) the RLPMs were tested to have positive and relatively endured effectiveness on mitigating crash risk in car-following measured by two surrogate safety indicators based on time-to-crash (TTC) and deceleration rate to avoid a crash (DRAC). The findings of this study suggest the RLPMs could be an especially applaudable form of perceptual markings as they are relatively effective in the long-term and are multifunctional in intervening speed, distance, headway, and crash risk. This study also emphasizes the challenge of more field tests and observations on the long-term performance of the perceptual markings, and the thorough considerations of the visual perception mechanism behind the markings to achieve an alternative solution to the long-term issue.

Integrated Crash Avoidance and Mitigation Algorithm for Autonomous Vehicles

This article presents a novel integrated path-following, crash avoidance, and crash mitigation control algorithm for autonomous vehicles. To improve stability and tracking accuracy of the algorithm in extreme conditions, combined-slip tire forces are considered in the system model. A predictive control framework that monitors slip conditions at each tire is then developed to achieve good dynamics performance by controlling active front steer and brake modulation at each corner. A novel switching mechanism that does not rely on a separate path generation module is designed for avoidance and mitigation phases, which is verified in various harsh driving conditions. Another strong point is the objective function for the crash mitigation phase that is developed based on real-world crash statistics. Simulation results confirm that the proposed algorithm can not only track the desired path in normal driving phase, but also avoid crash and reduce crash severity with ensured vehicle stability.

Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

Crash Mitigation in Motion Planning for Autonomous Vehicles

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.