Hierarchical Cooperative Control of Connected Vehicles:From Heterogeneous Parameters to Heterogeneous Structures

As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving safety.In this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures.By separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and structures.Within the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback control.Closed-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication topology.Numerical simulation and field experiment verify the effectiveness of the proposed method.

Genetic Algorithm‑Based SOTIF Scenario Construction for Complex Traffic Flow

The Safety of The Intended Functionality(SOTIF)challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle(AV),which leads to hazards.As for operationcontent-related features,the scenario is similar to AVs’SOTIF research and development.Therefore,scenario generation is a significant topic for SOTIF verification and validation procedure,especially in the simulation testing of AVs.Thus,in this paper,a well-designed scenario architecture is first defined,with comprehensive scenario elements,to present SOTIF trigger conditions.Then,considering complex traffic disturbance as trigger conditions,a novel SOTIF scenario generation method is developed.An indicator,also known as Scenario Potential Risk,is defined as the combination of the safety control intensity and the prior collision probability.This indicator helps identify critical scenarios in the proposed method.In addition,the corresponding vehicle motion models are established for general straight roads,curved roads,and safety assessment areas.As for the traffic participants’motion model,it is designed to construct the key dynamic events.To efficiently search for critical scenarios with the trigger of complex traffic flow,this scenario is encoded as genes and it is regenerated through selection,mutation,and crossover iteration processes,known as the Genetic Algorithm(GA).Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios,contributing to the construction of the SOTIF scenario library.