02169nas a2200265 4500008004100000245008000041210006900121260001200190300001300202520135300215653002801568653002101596100001901617700002201636700002201658700001801680700001901698700001801717700003101735700002201766700002301788700002301811700002101834856004801855 2020 eng d00aAre commercially implemented adaptive cruise control systems string stable?0 aAre commercially implemented adaptive cruise control systems str c06/2020 a12 pages3 a
In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. A total of seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in designed car-following experiments with ACC engaged by the following vehicle. The data is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the proprietary ACC systems. The string stability of the data fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In the multi-vehicle platoon test, an initial disturbance of 6 mph is amplified by 19 mph to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC and return control to the human driver. The data collected the driving experiments is made available, representing the largest available driving dataset on ACC equipped vehicles.
10aAdaptive Cruise Control10aString Stability1 aGunter, George1 aGloudemans, Derek1 aStern, Raphael, E1 aMcQuade, Sean1 aBhadani, Rahul1 aBunting, Matt1 aMonache, Maria, Laura Dell1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aPiccoli, Benedetto1 aWork, Daniel, B. uhttp://dx.doi.org/10.1109/TITS.2020.300068202407nas a2200157 4500008004100000020002200041245005700063210005600120260001000176300001200186520191000198100001902108700001802127700002302145856008102168 2019 eng d a978-1-119-55239-000aModel-based engineering with application to autonomy0 aModelbased engineering with application to autonomy bWiley a255-2853 aIn this chapter we focus on where models fit into the verification and validation design cycle of autonomous cyber-physical systems. These systems typically make decisions through myriad of sensing loops, have implementations in multiple languages, and may have their logic represented in several different kinds of formal models. The use of code generation, along with software-in-the-loop and hardware-in-the-loop simulation (discussed further in Section 4), permits system designers to apply various agile techniques for the validation and verification of systems as requirements are implemented, tested, and demonstrated. The work in this chapter explores such a design cycle with application to autonomous driving. Examples are given for the implementation of various components that describe vehicle dynamics, control models, system identification, sensor/data acquisition, etc., which can be functionally de- scribed in models, and explored in simulation before utilizing code generation to deploy final solutions. The integration of simulation tools during functional design, software-in-the-loop testing, and hardware-in-the-loop testing, permits regression evaluation of use case scenarios. In addition to functional testing, we also describe how high-level domain- specific models can be used to include verification-in-the-loop toolboxes as part of the design cycle. All the examples in this chapter are based on an autonomous Ford Escape, which has a Robotic Operating System (ROS) API for its control and the integration of autonomous components—however, the results are applicable to other event-based and time-triggered middleware platforms. The implementation models in use include Simulink, MATLAB, StateFlow, and other domain-specific languages that specify high-level behaviors.
1 aBhadani, Rahul1 aBunting, Matt1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/model-based-engineering-application-autonomy01253nas a2200241 4500008004100000245008200041210006900123520049300192653002800685653002100713100001900734700001300753700002200766700002100788700003100809700001900840700001800859700001900877700002300896700002200919700002300941856004700964 2019 eng d00aWiP Abstract: String stability of commercial adaptive cruise control vehicles0 aWiP Abstract String stability of commercial adaptive cruise cont3 aIn this work, we conduct a series of car-following experiments with seven different ACC vehicles and use the collected data to model the car-following behavior of each vehicle. Using a linear stability analysis, the string stability of each tested vehicle is analyzed. Addition- ally, platoon experiments with platoons of up to eight identical vehicles are conducted to validate the stability findings. Previously, only one commercial ACC system has been evaluated for string stability. 10aAdaptive Cruise Control10aString Stability1 aGunter, George1 aYang, Y.1 aStern, Raphael, E1 aWork, Daniel, B.1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matt1 aLysecky, Roman1 aSprinkle, Jonathan1 aSeibold, Benjamin1 aPiccoli, Benedetto uhttps://dl.acm.org/citation.cfm?id=3313325