An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

Abstract

Summary Collaborative and autonomous driving vehicles combine hardware and software complex processes, also are heavily dependent on and influenced by the world of physical and cyber interactions. They have enabled many new features and advanced functionalities, such as stochastic and hybrid natures, mobile spatial topologies, and time‐critical dependability. However, the existing modeling and verification techniques have not established faith in proving correctness and safety. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In order to ensure safety, we first design an accident prediction architecture in system design‐time and run‐time stages. We apply it on collaborative and autonomous overtaking systems involving spatial‐ and time‐critical accident predictions. Then, we develop a novel and dedicated spatio‐clock stochastic specification language (SCSSL) to describe safety invariants and guards in domain‐specific autonomous driving systems. Next, we create the spatio‐clock stochastic and hybrid automata models based on SCSSL in order to model inherently stochastic and hybrid behaviors. To illustrate the effectiveness of spatio‐clock consistency stochastic specification and verification, we adopt statistical model checking natively to provide reliable predictions for the incoming collision instants and positions. Finally, we present an illustrative overtaking case study to verify spatio‐clock stochastic and hybrid related properties and ensure correct modeling, and demonstrate the significance of our proposed approach.

Artificial Intelligence and Software Modeling Approaches in Autonomous Vehicles for Safety Management: A Systematic Review

“…The methodology necessitates further elaboration to specify intricate particulars to avoid collisions with stationary obstructions. In light of the distinctive movement patterns exhibited by vehicles, a suite of solutions is explored to govern vehicle placement within Wang et al [65], focused on optimizing mobility through temporal control of autonomous vehicles' positions. The method factors environmental conditions using probability-based equations derived from collision and accident likelihood over different time intervals.…”

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

“…Dumakor-Dupey et al 36 ), railways (Alawad et al 37 ; Gao et al 38 ), autonomous vehicle safety (Wang et al 39 ), petrochemical industry (Mugunthan 40 ;…”

“…Several authors have used ML to predict occupational accidents analysis in different sectors, in the aviation industry (Herrema et al 26 ; Burnett and Si 27 ), the mining industry (Rivas et al 22 ; Matias et al 28 ), construction industry (Tixier et al 4 ; Goh and Ubeynarayana 6 ; Chokor et al 29 ; Mistikoglu et al 30 ; Liao et al 31 ; Zhu et al 32 ; Choi et al 33 ), in steel and other mineral extraction industry (Sarkar et al 8,11,16,23 ; Shirali et al 34 ; Yang et al 35 ; Dumakor‐Dupey et al 36 ), railways (Alawad et al 37 ; Gao et al 38 ), autonomous vehicle safety (Wang et al 39 ), petrochemical industry (Mugunthan 40 ; Balasubramanian and Thangamani 41 ; Kurian et al 21,42 ; Xu et al 43 ; Cakir et al 44 ; Tamascelli et al 5 ). According to the findings, accident prediction models in the oil and gas industry are still in their early stages.…”