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1. Crashing Waves: An Empirical Vehicle-to-Barrier Communication Channel Model via Crash Tests

   https://doi.org/10.1109/MASS52906.2021.00059  

   Lunar, Mohammad M.; Stolle, Cody; Faller, Ronald K.; Vuran, Mehmet C. (October 2021, 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS))
2. Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

   https://doi.org/10.1016/j.aap.2023.107021  

   Ren, Ran; Li, Hang; Han, Tianfang; Tian, Chi; Zhang, Cong; Zhang, Jiansong; Proctor, Robert W.; Chen, Yunfeng; Feng, Yiheng (June 2023, Accident Analysis & Prevention)
3. Semantic crash bucketing

   https://doi.org/10.1145/3238147.3238200  

   van Tonder, Rijnard; Kotheimer, John; Le Goues, Claire (January 2018, Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering)

   Precise crash triage is important for automated dynamic testing tools, like fuzzers. At scale, fuzzers produce millions of crashing inputs. Fuzzers use heuristics, like stack hashes, to cut down on duplicate bug reports. These heuristics are fast, but often imprecise: even after deduplication, hundreds of uniquely reported crashes can still correspond to the same bug. Remaining crashes must be inspected manually, incurring considerable effort. In this paper we present Semantic Crash Bucketing, a generic method for precise crash bucketing using program transformation. Semantic Crash Bucketing maps crashing inputs to unique bugs as a function of changing a program (i.e., a semantic delta). We observe that a real bug fix precisely identifies crashes belonging to the same bug. Our insight is to approximate real bug fixes with lightweight program transformation to obtain the same level of precision. Our approach uses (a) patch templates and (b) semantic feedback from the program to automatically generate and apply approximate fixes for general bug classes. Our evaluation shows that approximate fixes are competitive with using true fixes for crash bucketing, and significantly outperforms built-in deduplication techniques for three state of the art fuzzers. 

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4. Spatio-Temporal Crash Prediction: Effects of Negative Sampling on Understanding Network-Level Crash Occurrence

   https://doi.org/10.1177/0361198121991836  

   Way, Peter; Roland, Jeremiah; Sartipi, Mina; Osman, Osama (February 2021, Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   In projects centered around rare event case data, the challenge of data comprehension is greatly increased because of insufficient data for deriving insight and analysis. This is particularly the case with traffic crash occurrence, where positive events (crashes) are rare and, in most cases, no data set exists for negative events (non-crashes). One method to increase available data is negative sampling, which is the process of creating a negative event based on the absence of a positive event. In this work, four negative sampling techniques are presented with varying ratios of negative to positive data. These types of techniques are based on spatial data, temporal data, and a mixture of the two, with the data ratios acting as class balancing tools. The best performing model found was with a negative sampling technique that shifted temporal information and had an even 50/50 data split, with an F-1 score, a formulaic combination of precision and recall, of 93.68. These results are promising for Inteligent Transportation Systems (ITS) applications to inform of potential crash locations in an entire area for proactive measures to be put in place. 

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5. Research for Practice: Crash Consistency

   https://doi.org/10.1145/3570521  

   Alagappan, Ramnatthan; Alvaro, Peter (January 2023, Communications of the ACM)

   Keeping data safe in the presence of crashes is a fundamental problem. 

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