Multi-attribute decision making on mitigating a collision of an autonomous vehicle on motorways

Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles

Prospective and retrospective performance assessment of Advanced Driver Assistance Systems in imminent collision scenarios: the CMI-Vr approach

Structured abstract Introduction Prospective and retrospective performance assessment of Advanced Driver Assistance Systems (ADASs) is fundamental to pilot future enhancements for active safety devices. In critical road scenarios between two vehicles where ADAS activation enables collision mitigation only, currently available assessment methodologies rely on the reconstruction of the impact phase consequent to the specific intervention on braking and steering: the velocity change sustained by the vehicle in the collision ($$\Delta V$$ Δ V ) is retrieved, so that IR decrease for the vehicle occupants can be obtained by appropriate Injury Risk (IR) models. However, information regarding the ADAS performance is available only after the impact phase reconstruction and not just as when the criticality occurs in the pre-impact phase: the best braking and steering alternative cannot be immediately envisaged, since a direct correlation lacks between the braking/steering intervention and IR. Method This work highlights an ADAS performance assessment method based on the disaggregation of $$\Delta V$$ Δ V in the two pre-impact parameters closing velocity at collision ($$V_r$$ V r ) and impact eccentricity, represented by the Crash Momentum Index (CMI). Such a disaggregation leads to the determination of IR based solely on impact configuration between the vehicles, without directly considering the impact phase. The performance of diverse ADASs in terms of intervention logic are directly comparable based on the resulting impact configuration, associated with a single coordinate in the CMI-$$V_r$$ V r plane and a sole IR value as a consequence. Results The CMI-$$V_r$$ V r approach is employable for both purposes of prospective and retrospective performance assessment of ADAS devices. To illustrate the advantages of the methodology, a solution for prospective assessment based on the CMI-$$V_r$$ V r plane is initially proposed and applied to case studies: this provides direct suggestions regarding the most appropriate interventions on braking and steering for IR minimization, fundamental in the tuning or development phase of an ADAS. A method for retrospective assessment is ultimately contextualized in the EuroNCAP "Car-to-Car Rear moving" test for an Inter-Urban Autonomous Emergency Braking system, a device implemented on a significant portion of the circulating fleet. Conclusions Based on the evidenced highlights, it is demonstrated that the approach provides complementary information compared to well-established performance assessment methodologies in all stages of an ADAS life cycle, by suggesting a direct physical connection in the pre-impact phase between the possible ADAS interventions and the foreseeable injury outcomes

Priorities and Potential of Pedestrian Protection - Accident data, Experimental tests and Numerical Simulations of Car-to-Pedestrian Impacts

Pedestrian disability and fatality as a consequence of car crashes is a large global health problem. To introduce maximally effective car-based countermeasures it is important to understand which injuries are most common and from which car parts they originate. It is also important to focus on the most severe injuries resulting in disability or death. The aim of this thesis was therefore to determine priorities for and evaluate the potential of car-mounted safety systems designed to mitigate severe upper-body injuries (including disability and fatality) of pedestrians in car crashes. Accident data was collected from two areas; severe (AIS3+) accidents in Dresden/Hannover in Germany and fatal accidents in Sweden. For the surviving pedestrians an estimate of long-term injury was performed using accident data- derived risk matrices of permanent injury. Results showed that 31% would sustain a permanent impairment of some kind and 5% would sustain a more severe impairment, where the head was most susceptible to severe impairment. The car front frequently caused leg injuries, which is addressed in current regulations. However, current legal tests do not address the most common upper-body injury source, the windshield, which was found to be the dominating cause of head injuries. Chest injuries, frequently caused by both the hood and windshield areas in the severe and fatal crashes in this thesis, are also unaddressed in legal tests. Children are most commonly head-injured from the hood area, which is addressed in current regulations. Further, regulations do not fully consider brain injury with the current head test methods. Therefore, in this thesis focus was on upper-body injury/source combinations not addressed in the regulations, that is, the head-to-windshield area and chest-to-hood/windshield areas, and the evaluation of brain injury in hood and windshield impacts. Experimental head-to-hood component tests with succeeding brain simulations were performed to evaluate the influence of the under-hood distance and head impact speed. A hood designed to minimize linear head loading to acceptable injury levels was also found effective in reducing combined linear/rotational brain loading. Further, in full-scale car-to-pedestrian finite element simulations both a braking and deployable system alone proved efficient in reducing head and chest loading, and an integrated countermeasure of combining the two systems proved to increase the protection potential. While current pedestrian countermeasures focus on the head-to-hood impact, this thesis recommends extending countermeasures to the lower part of the windshield and the A-pillars, and adding brain and chest injury assessment for both hood and windshield areas to effectively minimize disabling and fatal injuries. Since head impact location and head impact speed is dependent on the car design, the introduction of full-scale simulations in the test methods to determine impact conditions for experimental component tests is recommended. If the deployable countermeasures are combined with autonomous braking in an integrated system the most effective system is achieved. Auto-brake systems should, in high speed impacts, aim to reduce speeds to where the secondary countermeasures can effectively mitigate injury. Future pedestrian test methods should therefore evaluate how primary and secondary countermeasures interact

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits

Car crashes with two-wheelers in China: Proposal and assessment of C-NCAP automated emergency braking test scenarios

In China, around 15,000 users of two-wheelers (TWs) die on the road every year. Passenger cars are the dominating crash opponent of TWs in road traffic crashes. Understanding the characteristics of car crashes with TWs is essential to enhance cars’ safety performance and improve the safety of TW riders in China. This thesis has three objectives. First, to define test scenarios of Automated Emergency Braking systems for cars encountering TWs (TW-AEB) in China (Paper I). Second, to assess whether cars with good ratings in consumer safety rating programs (e.g., New Car Assessment Program: NCAP) are also likely to perform well in the real-world. Finally, to understand the characteristics of the car crashes with TWs after the TW-AEB application. To achieve the first objective, cluster analysis was applied to the China In-Depth Accident Study (CIDAS). The results were six test scenarios (Paper I), which are proposed for the Chinese NCAP (C-NCAP) TW-AEB testing. To achieve the second and third objectives, counterfactual virtual simulations were performed with and without TW-AEB to a) a C-NCAP TW-AEB test scenario set ; b) an alternative scenario set based on the results of Paper I; and c) real-world crashes in China. Results show much higher crash avoidance rate and lower impact speed were found for C-NCAP scenario set than for the other two sets. To better reflect car crashes with TW in China, longitudinal same-direction scenarios with the car or TW turning and perpendicular scenarios with high TW traveling speed are recommended to be included in C-NCAP future releases. Future work will focus on assessing the combined benefit of preventive and protective safety systems for car-to-TW crashes in China

A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability during Hurricane Evacuation

Hurricane evacuation has become a major problem for the coastal residents of the United States. Devastating hurricanes have threatened the lives and infrastructure of coastal communities and caused billions of dollars in damage. There is a need for better traffic management strategies to improve the safety and mobility of evacuation traffic. In this study hurricane evacuation traffic was simulated using SUMO a microscopic traffic simulation model. The effects of Connected and Autonomous Vehicles (CAVs) and Autonomous Vehicles (AVs) were evaluated using two approaches. (i) Using the state-of-the-art car-following models available in SUMO and (ii) a co-simulation study by integrating the microscopic traffic simulation model with a separate communication simulator to find the realistic effect of CAVs on evacuation traffic. A road network of I-75 in Florida was created to represent real-world evacuation traffic observed in Hurricane Irma s evacuation periods. Simulation experiments were performed by creating mixed traffic scenarios with 25, 50, 75 and 100 percentages of different vehicle technologies including CAVs or AVs and human-driven vehicles. HDV Simulation results suggest that the CACC car-following model, implemented in SUMO and commonly used in the literature to represent CAVs, produces highly unstable results On the other hand the ACC car following model, used to represent AVs, produces better and more stable results. However, in a co-simulation study, to evaluate the effect of CAVs in the same evacuation traffic scenario, results indicate that with 25 percentage of CAVs the number of potential collisions decrease up to 42.5 percentage

Simulated surrogate measures to assess the effectiveness of countermeasures at signalized intersections

The traditional method for assessing safety conditions at signalized intersections depends on historical crash data. Difficulty and long waits for data collection as well as lack of reliability, represent some limitations. As a result of safety evaluation using traditional methods, countermeasures may be proposed to improve the degree of safety. This paper aims to assess the effectiveness of countermeasures at signalized intersections using micro-simulation model (VISSM10) software and the Surrogate Safety Assessment Model (SSAM) to deal with traffic conflicts as surrogate measures rather than crash data. The study relied on VISSIM10 to create a trajectory file as input of SSAM to conduct a traffic safety assessment using traffic conflict indicators of time to collision (TTC). Four four-legged signalized intersections in the city of Diwaniya are chosen to assess safety and then propose appropriate countermeasures. Different countermeasures are tested through simulation to estimate their effectiveness using two measures: the increase in time to collision and the percentage reduction in traffic conflicts. The results showed that model calibration reduced the mean absolute error of prevention (MAPE) and improved the fit between both the actual conflicts and simulated conflicts. A validation simulation has been performed compared with the observed conflict. According to the linear regression the number that simulated conflicts which highly related to the number of actual conflicts. Additionally, R2 can be described by the difference in simulated conflicts. Results go with effectiveness based on crash data and promising for unknown ones

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

SAFETY AND OPERATIONAL ASSESSMENT OF COOPERATIVE DRIVING SYSTEMS ON ROUNDABOUTS

This paper presents a simulation-driven method for assessing the safety and efficiency of traffic at roundabouts incorporating connected and automated vehicle (CAV) technology. Utilizing the newly proposed CAV-based factors specified by the Highway Capacity Manual (HCM) provided a practical framework for analyzing capacity dynamics across various traffic scenarios. Using microscopic traffic simulation on a roundabout model replicating real-world geometry and traffic attributes facilitated the identification of crucial behavioral parameters. This simulation spanned from smooth traffic scenarios to operational saturation, aiding in the study of mixed traffic scenarios during the transition to increasing CAV presence. Additionally, the study assessed the safety and traffic impact of a dedicated CAV lane using surrogate safety metrics. Aimsun software aided in model parameter calibration, which, combined with the Surrogate Safety Assessment Model (SSAM), supported safety analysis. Despite observed enhancements in roundabout performance with CAV integration, the benefits of a designated CAV lane highlighted the potential to reduce conflicts among vehicles. In conclusion, the paper emphasizes the overall performance enhancement achieved with CAVs at roundabouts while also providing insights for evaluating the potential of CAV technologies in future mobility management strategie