Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Expressway Traffic Flow Model Study Based on Different Traffic Rules

In this paper, two cellular automata traffic models are proposed to simulate the operation of an expressway. The results show that the flow rate and the average velocity are generally equal in the same density which is different among the lanes. The analysis of lane changing times and the velocity total deviation show some characteristics which are difficult to explain phase transitions under fundamental diagram theory. Therefore,the concept of lane changing probability is introduced, and it is concluded that the speed-limit rule can reduce the motivation of lane changing effectively.

Trajectory analysis at intersections for traffic rule identification

In this paper,we focus on trajectories at intersections regulated by various regulation types such as traffic lights,priority/yield signs,and right-of-way rules.We test some methods to detect and recognize movement patterns from GPS trajectories,in terms of their geometrical and spatio-temporal components.In particular,we first find out the main paths that vehicles follow at such locations.We then investigate the way that vehicles follow these geometric paths(how do they move along them).For these scopes,machine learning methods are used and the performance of some known methods for trajectory similarity measurement(DTW,Hausdorff,and Fréchet distance)and clustering(Affinity propagation and Agglomerative clustering)are compared based on clustering accuracy.Afterward,the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories.We show that depending on the regulation type,different movement patterns are observed at intersections.This finding can be useful for intersection categorization according to traffic regulations.The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information(traffic signs,traffic lights,etc.).

SOTIF risk mitigation based on unified ODD monitoring for autonomous vehicles

Purpose–The purpose of this paper is to design a unified operational design domain(ODD)monitoring framework for mitigating Safety of the Intended Functionality(SOTIF)risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.Design/methodology/approach–A unified model of ODD monitoring is constructed,which consists of three modules:weather condition monitoring for unusual weather conditions,such as rain,snow and fog;vehicle behavior monitoring for abnormal vehicle behavior,such as traffic rule violations;and road condition monitoring for abnormal road conditions,such as road defects,unexpected obstacles and slippery roads.Additionally,the applications of the proposed unified ODD monitoring framework are demonstrated.The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.Findings–First,the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework.Second,the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework.Third,the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.Originality/value–The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.