The rapid development of vehicle‐to‐vehicle (V2V) communication technology provides more opportunities to improve traffic safety and efficiency, which facilitates the exchange of multivehicle information to mine potential patterns and hidden associations in vehicle trajectory prediction. To address the importance of fine‐grained vehicle interaction modelling in vehicle trajectory prediction, the present work proposes an integrated vehicle trajectory prediction model that combines the multihead attention mechanism with long short‐term memory (IMA‐LSTM) in multivehicle interaction scenarios. Compared to existing studies, a dedicated feature extraction module including both individual features and interactive features is designed and sophisticated multihead attention mechanism is applied with LSTM framework to capture the variation of spatial‐temporal interactions between vehicles. The performance of the proposed model in different scenarios is examined using both the high‐D and the NGSIM dataset through comprehensive comparison experiments. The results indicate that the proposed IMA‐LSTM model presents great improvement in vehicle trajectory prediction performance in different scenarios compared to the model that does not consider multivehicle interaction features. Moreover, it outperforms other existing models in 3–5s prediction horizons and such superiority is more evident in left lane‐changing (LLC) scenarios than lane‐keeping (LK) and right lane‐changing (RLC) scenarios. The outcomes fully address the importance of fine‐grained interactive feature modelling in improving vehicle trajectory performance in complex multivehicle interaction scenarios and could further contribute to more refined traffic safety and traffic efficiency management.

Taint analysis has been widely applied in ex post facto security applications, such as attack provenance investigation, computer forensic analysis, and reverse engineering. Unfortunately, the high runtime overhead imposed by dynamic taint analysis makes it impractical in many scenarios. The key obstacle is the strict coupling of program execution and taint tracking logic code. To alleviate this performance bottleneck, recent work seeks to offload taint analysis from program execution and run it on a spare core or a different CPU. However, since the taint analysis has heavy data and control dependencies on the program execution, the massive data in recording and transformation overshadow the benefit of decoupling. In this paper, we propose a novel technique to allow very lightweight logging, resulting in much lower execution slowdown, while still permitting us to perform full-featured offline taint analysis. We develop StraightTaint, a hybrid taint analysis tool that completely decouples the program execution and taint analysis. StraightTaint relies on very lightweight logging of the execution information to reconstruct a straight-line code, enabling an offline symbolic taint analysis without frequent data communication with the application. While StraightTaint does not log complete runtime or input values, it is able to precisely identify the causal relationships between sources and sinks, for example. Compared with traditional dynamic taint analysis tools, StraightTaint has much lower application runtime overhead.