00876nas a2200277   4500008004100000245009100041210006900132260001200201490000700213653002400220653002700244100002200271700001500293700003100308700001900339700002100358700002100379700002400400700002100424700001500445700002300460700002200483700002300505700002100528856004900549       2018                        eng d00aDissipation of stop-and-go waves via control of autonomous vehicles: Field experiments0 aDissipation of stopandgo waves via control of autonomous vehicle  c04/20180 v8910aautonomous vehicles10acyber physical systems1 aStern, Raphael, E1 aCui, Shumo1 aMonache, Maria, Laura Dell1 aBhadani, Rahul1 aBunting, Matthew1 aChurchill, Miles1 aHamilton, Nathaniel1 aPohlmann, Hannah1 aWu, Fangyu1 aPiccoli, Benedetto1 aSeibold, Benjamin1 aSprinkle, Jonathan1 aWork, Daniel, B.  uhttps://csl.arizona.edu/stern2017dissipation06037nas a2200253   4500008004100000245005300041210005300094520525700147100002205404700001505426700002105441700002105462700003105483700002305514700002105537700001505558700002205573700002405595700002105619700001905640700002105659700002305680856008005703       2016                        eng d00aDampening traffic waves with autonomous vehicles0 aDampening traffic waves with autonomous vehicles3 aIn congested traffic, minor disturbances or fluctuations in the velocity of a single vehicle may induce dynamically evolving traffic waves such as stop-and-go waves. These waves cause vehicles upstream to slow down or stop before accelerating back to the desired speed, resulting in increases in fuel consumption and risk of collisions. This work postulates that by intelligently controlling a small number (e.g., 1-5%) of autonomous vehicles (AVs) soon to be present in the traffic flow, it is possible to dampen or completely remove these speed fluctuations in the entire traffic stream. By only making small changes to the way the AV drives compared to human drivers near the dynamic wave, we can significantly improves the smoothness of the overall traffic flow, and reduces fuel consumption of all vehicles on the road. Due to the inherent instability of dense traffic flow, small disturbances in the speed of individual vehicles can generate large-scale disturbances in the traffic stream in the form of waves. Uncontrolled, these waves will propagate indefinitely until the traffic density decreases and the instability dissipates. This phenomenon was first experimentally demonstrated in the famous ring road experiment of Sugiyama et al., 2008. In that experiment, 22 vehicles were driven on a circular track to demonstrate that the uniform initial traffic flow (uniform speed and spacing) quickly devolves into a stop-and-go wave with vehicles at one side of the track at a complete standstill, while vehicles at the other side of the track are racing to keep up with the vehicle in front of them. To learn effective AV control strategies to dampen these traffic waves we must accurately simulate traffic in the specific conditions under which these waves arise. This will allow us to study how traffic responds to different control mechanisms implemented by the AV mathematically from a control prospective as well as in simulation. In this work, a microscopic car following model is used to simulate traffic with a mix of human-controlled and autonomous vehicles. We model human traffic flow using the combined optimal-velocity follow-the-leader (OV-FTL). This model is calibrated using trajectories of vehicles under human driving behavior such that the macroscopic quantities (average velocity, wave growth time, wave propagation speed) match those quantities observed in the Sugiyama experiment. Note that thus, the microscopic model is calibrated to reproduce real traffic waves, which is not commonly done in the traffic modelling community. Using linear stability theory, this model is shown to be unstable, since it has positive-valued eigenvalues. These manifest themselves in the form of stop-and-go waves when the calibrated model is used to simulated individual vehicle’s trajectories in time. In order to calibrate more realistic models of human drivers in dense, unstable, traffic conditions, field experiments are conducted using between 12 and 22 vehicles at the University of Illinois in Urbana, Illinois to re-create the traffic waves observed in the Sugiyama experiment, and probe the state space of traffic conditions under which such traffic instabilities will arise. This data is then used to calibrate more realistic models of human driving behavior that cover a broader range of traffic conditions. All vehicles used in the experiments are equipped with onboard diagnostics (OBD-II) scanners to record the vehicle’s velocity, engine speed, fuel rate, and fuel consumption throughout the experiment. This provides additional data that allows us to compare fuel consumption in traffic with stop-and-go waves to uniformly-flowing traffic. Furthermore, the trajectory of each vehicle is tracked using a 360-degree panoramic camera located at the center of the circular track. To begin, this research addresses the case of the 22-vehicle system recorded in the Sugiyama experiment and augment it by replacing one of the vehicles with an AV, which provides actuation in the system since it can be controlled to drive arbitrarily smoothly within the constraints set by the vehicle immediately in front of it. To apply linear stability theory, this augmented system is then linearized about an equilibrium traffic flow. We then use a feedback controller and pole placement to stabilize the system, and prevent traffic waves from emerging. Results from simulating the stabilized system in time indicate that a single AV using realistic control gains is able to dampen traffic waves in a 22-vehicle system without decreasing the average speed. The societal implications of this work are broad since most drivers experience delays and increased fuel consumption due to unstable and non-uniformly flowing traffic. While complete automation of the entire vehicle fleet may be many years away, in the short term, it is likely that some vehicles will be capable of driving autonomously in the near future. This research demonstrates then even with only a small percentage of vehicles driving autonomously, it is possible alter the traffic flow and prevent instabilities from arising. This results are lower fuel consumption and a shorter driving time not only for the autonomous vehicles, but all vehicles in the traffic stream.1 aStern, Raphael, E1 aWu, Fangyu1 aChurchill, Miles1 aWork, Daniel, B.1 aMonache, Maria, Laura Dell1 aPiccoli, Benedetto1 aPohlmann, Hannah1 aCui, Shumo1 aSeibold, Benjamin1 aHamilton, Nathaniel1 aHaulcy, R’mani1 aBhadani, Rahul1 aBunting, Matthew1 aSprinkle, Jonathan  uhttps://csl.arizona.edu/content/dampening-traffic-waves-autonomous-vehicles