Automobile Damage Research Articles

자율주행자동차 상용화에 대비한 자동차보험제도 연구

On May 7, 2016, in the state of Florida, USA, a fatal accident occurred involving an autonomous vehicle and a large truck, leading to the death of the driver. This incident heightened interest in autonomous vehicles and sparked discussions on their implications, including the challenges and liability issues in the event of accidents during autonomous operation. The application of existing legal frameworks, such as Article 750 of the Civil Code on tort liability and Article 3 of the Automobile Damage Compensation Guarantee Act on operator liability, to accidents involving autonomous vehicles raises questions about identifying responsible parties and compensating victims. This study examines the applicability of these legal principles to accidents occurring during autonomous driving and proposes the introduction of a No-Fault insurance system as a novel automotive insurance mechanism to address these issues. The analysis revealed that the No-Fault insurance system, a solution to the problems posed by the current fault-based liability system and already implemented in several advanced countries since the early 2000s, was considered but ultimately not adopted in South Korea due to challenges in amending existing legislation, limitations in calculating insurance rates, and concerns over moral hazard, among other issues. However, as autonomous vehicles are poised to integrate into our lives more rapidly than anticipated, and the social concerns regarding traffic accidents during autonomous operation deepen, it is imperative to form a task force comprising the Ministry of Land, Infrastructure, and Transport, the Financial Supervisory Service, the General Insurance Association, among others, before it's too late. This task force should engage in research and discussions, while building a consensus among the public, to prepare for the new automotive market driven by the autonomous vehicle industry.

Damage Detection of An Automobile

As a result of the proliferation of automobile in- dustries today. There have been an increasing number of car accidents, not all of which are serious, but the automobile is damaged. Detecting automobile damage at the site of an accident using images is exceptionally beneficial as it may significantly lower the cost of processing the insurance reimbursement process while also providing more convenience to automobile users. In most cases, this damage is detected and assessed manually from the car’s images during the car evaluation process. In this paper, we worked on the problem of automation of vehicle damage detection which can be used by insurance companies to automate the process of vehicle insurance claims in a rapid fashion. The recent advances in computer vision largely due to the adoption of fast, scalable, and end-to-end trainable Convolutional Neural Networks make it technically feasible to recognize vehicle damages using semantic segmentation. We manually collected and annotated images from various online sources containing different types of vehicle damages and we used U-NET architec- ture to detect the damage of an automobile.

Deep Convolutional Neural Networks With Transfer Learning for Automobile Damage Image Classification

Deep learning models are more capable of handling large and complex datasets that generally appear in the insurance industry than traditional machine learning models. In this study, transfer learning was employed to build and optimize a simulated automobile damage assessment system. Several classic deep learning methods were applied to extract features from original and augmented automobile damage images. Then, traditional machine learning and cross-validation techniques were applied to train and validate the system. The proposed deep learning model demonstrated advantages over traditional machine learning models regarding features extraction and accuracy. Deep learning approaches fused with logistic regression and support vector machine were found performing as well as those with artificial neural networks under two simulated scenarios. With the proposed method, automobile damage images can be evaluated for insurance adjustment purposes automatically, based on the acquired input. Hence, insurers can automate the claim and adjustment process, thereby achieving cost and time savings.

Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification

Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.

Underwriting systematic risk and profit margin in fuzzy CAPM and ICAPM models: examples of automobile and aviation risk

The fuzzy capital asset pricing model (CAPM) and fuzzy insurance capital asset pricing model (ICAPM) are used to investigate the underwriting systematic risk (βu ) and underwriting profit margin (UPM) in the coverage of automobiles and aviation. Results show that the best-fitting parameter of skew factor is asymmetric. There is a positive relationship between the return rate of underwriting and market in the fuzzy CAPM, fuzzy ICAPM, and crisp environment. From the risk map point of view, the values of βu of automobile damage with high loss frequency and low loss severity are larger than those in aviation loss with low loss frequency and high loss severity, and have below-normal market risk. The value of the coefficient of variation of UPM in the aviation line is larger than in the automobile line. Our calculations show that the variation of UPM from the fuzzy ICAPM is more suitable for forecasting the insurance financial data and the difference of loss types in insurance lines than the fuzzy CAPM.

An evaluation of fuzzy transportation underwriting systematic risk

From the insurance financial pricing point of view, we investigate the fuzzy transportation underwriting systematic risk made by major lines of transportation, including the insurances of automobile damage, automobile liability, compulsory automobile liability, compulsory motorcycle liability, marine hull, marine cargo, fishing vessel and aviation, in which the parameters of membership function may be the symmetric or asymmetric triangular fuzzy numbers. Our results show that the best-fitting parameters of the model from a company data are the asymmetric triangular fuzzy numbers in transportation underwriting systematic risk. We determinate the lower and upper limits of the fuzzy transportation underwriting systematic risk associated to these fuzzy numbers and show that the underwriting of most lines are below normal market risk in Taiwan, except that of the compulsory automobile liability. We also estimate the underwriting beta value for the transportation various lines and show that they may be positive or negative. It also sees that the transportation underwriting systematic risk in the fuzzy environment is larger than that in the crisp environment. Especially, we have found the ranking with best-fitting of skew factors and used them to evaluate the underwriting risk in each line, which may help us to perform the forecasting of the underwriting systematic risk and underwriting profit margin in transportation lines and to determine the appropriateness of the resulting premiums. While the investigations are the special properties of our input data of Taiwan the prescription of this paper could be apply to the transportation lines of other countries.

Factors influencing the injury pattern and injury severity after high speed motor vehicle accident—A retrospective study

Motor vehicular accidents (MVA) are the leading cause of death among people under 40 years of age. Despite improvement in car safety and driver awareness of the use of safety devices, fatalities and severe injuries continue to occur. From 1997 to 2004, 13,678 patients after MVA were admitted to our institution. Out of this cohort, 584 (4.3%) patients suffered blunt major trauma defined as Injury Severity Score (ISS) >or=16 and at least one life-threatening injury in one body region. Preclinical data were recorded in 458 patients matching the inclusion criteria. The circumstances of the trauma scene such as weather conditions were analysed as well as technical crash data such as direction of impact, security devices used and type and severity of automobile damage. In a retrospective trial, the influence of preclinical variables on the injury pattern and on injury severity was investigated. 314 (68.6%) patients were male and 144 (31.4%) female. Injury severity (p=0.015) and rate of multiple injuries (p=0.012) were higher in patients after side-impact crashes. If automobiles with SIPS were used, injury severity was significantly reduced in case of side-impact crashes (p=0.003). Patients after frontal impact crashes had a higher rate of severe traumatic brain injury (TBI) compared to the overall cohort (p=0.014). Patients suffering blunt aortic (n=29) dissection were involved in frontal crashes with seat belt use (p<0.001). If patients were entrapped, injury severity (p=0.021) and rate of multiple injuries (p=0.018) were significantly higher. Rear-end collisions with trucks without rear protection led to higher mortality rates (p=0.011). According to our data significant association between technical crash data and injury pattern and injury severity can be assumed. In case of high speed MVA in rural areas the trauma mechanism and the circumstances (i.e., impact direction, automobile deformation) should be considered to identify patients at high risk of severe blunt trauma and multiple injuries.

Underwriting profit margin of P/L insurance in the fuzzy-ICAPM

The ICAPM is used to study the underwriting profit margin of the P/L insurance company, including the insurances of automobile damage, automobile liability and fire, in which the parameters are the symmetric or non-symmetric triangular fuzzy numbers. From the ten-year data of a company in Taiwan we determine the lower and upper limits associated with the various α-level of the fuzzy numbers. Our results show that the best-fitting parameters of the model from our data are the asymmetric triangular fuzzy numbers. The skew factors in each insurance are determined, which could be used to perform the forecasting of the underwriting profit margin. Our results show that the systematic risk in the fuzzy environment (with best-fitting value of skew factor) becomes larger than that in the crisp environment. However, the insurance underwriting leverage and insurance financial leverage in the fuzzy environment are smaller than those in the crisp environment.

British Columbia's Need for No Fault Automobile Insurance

The effects, under the present system of automobile insurance, of three-party coverage of income loss and two party coverage of automobile damage are explored in this study. A large sample survey has been conducted and the distribution of compensation is found to be biased against the serious injury and fatality cases. These are the large-loss cases which are forced by the present system to rely on three-party insurance for compensation. This discrimination is shown to affect public opinion of the insurance industry adversely and very significantly. The advantages of extending no fault insurance to include income loss are discussed.