Download - Stability of CACC Vehicle Platoons in a Semi-autonomous Driving Environment. H. Michael Zhang July 1, 2014 University of Science and Technology of China,

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Stability of CACC Vehicle Platoons in a Semi-autonomous Driving

Environment.

H. Michael ZhangJuly 1, 2014

University of Science and Technology of China, Hefei, China

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Outline

• Basic concepts of ACC/CACC Control• Local and String Stability• Simulation Tool (VENTOS)• Simulation Results• Conclusions

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Three levels of automation

• Manual driving (free agent)– human sensing and human control

• Adaptive Cruise Control (ACC)– sensing + automated longitudinal control

• Cooperative Adaptive Cruise Control (CACC)– sensing +V2V communication + automated longitudinal

control• Autonomous vehicles– Sensing, communication and lateral and longtidinal

control are fully automated, humans not in the loop

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ACC/CACC vehicle streams

• Single vehicle control

• Vehicle platooning Platoon Leader

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A mixed traffic stream with manual, ACC, and CACC driving

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ACC/CACC control system

• Upper level controller:– Speed control (SC) mode– Gap control (GC) mode– Collision avoidance (CA) mode

• Lower level controller:• latency with vehicle dynamics

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Speed Control (SC) Mode

• When space gap is adequate for driving with intended speed, vehicles operate in this SC mode. In this mode, vehicles will operate based on the difference between its current speed and its intended speed.

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Gap Control (GC) Mode

• Vehicles will operate in GC mode when following a close-by preceding vehicle. Upper level controller is designed to maintain spacing with constant time gap to its preceding vehicle.

• For ACC, desired acceleration is determined by error of current space gap with desired spacing and speed difference between current and preceding vehicle.

• For CACC, preceding vehicle acceleration is also available through wireless communication.

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Collision Avoidance (CA) Mode

• When space gap is smaller than safe gap , vehicle will switch to CA mode. Maximum deceleration will immediately be applied to avoid collision.

• Safe gap is determined by speed and maximum deceleration ability of current and preceding vehicle:

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Three control modes

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ACC/CACC STABILITY ANALYSIS

• The ACC/CACC control laws result in linear dynamical systems

• Laplace/Fourier transforms are usually used to study the stability of these systems

• The transfer function ( H(s) ) approach is used here– Local stability: poles of the transfer function has

negative real parts– String stability: |H(jw)| < 1

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• ACC Gap Control:– Criterion for local stability

– Criterion for string stability

is both necessary and sufficient to have some positive and to ensure both local and string stability.

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• CACC Gap Control:

• Normalize and set:

• Transfer function:

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CACC gap control mode design

• Local stability:• string stability:

• Considering stability and magnitude of overshoot and acceleration input, gains for CACC is set to be:

• is required for local and string stability.

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Bode plot of ACC and CACC gap control mode with different time gap. In ACC, time gap increases from black to grey with 0.4 s interval from 0.6 s to 1.8 s. In CACC, time gap increases with 0.2 s interval from 0.3 s to 0.9 s. Larger time gap ensures better string stability.

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CACC Stability with Packet Loss

• When packet loss occurs in imperfect wireless communication, previously stored acceleration information will be used to determine the desired acceleration.

• Approximate communication packet loss as increased time delay, we can estimate the maximum packet loss ratio allowed as:

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IMPLEMENTATION OF ACC/CACC IN VENTOS

• VENTOS (VEhicular NeTwork Open Simulator) is a new open source integrated simulation framework based on SUMO and OMNET++ /Veins.

• SUMO (Simulation of Urban Mobility) is an open source, microscopic, continuous-space, discrete-time road traffic simulator and adopted as our traffic simulator.

• OMNET++ is an open-source, component-based simulation package and captures the wireless communication simulation in VENTOS.

• Traffic simulation in SUMO and network simulation in OMNET++ are tightly coupled by Traffic Control Interface (TraCI) and perform simultaneously in a closed-loop.

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CACC Beaconing

• CACC vehicles broadcast beacon packets periodically with broadcast interval of 0.1s.

• Beacon message includes: Sender address, Receiver address, Position, Acceleration, Lane ID.

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Simulation RESULTS

• Our simulation is done in a long single-lane freeway with speed limit of 30 m/s.

• A queue of vehicles is formed in a warm-up phase where vehicles enter the simulation area with different departure times, and eventually queue-up and follow each other.

• The first vehicle is referred to as veh0 and its speed profile is given and shown with a dashed line in figures. Other vehicles follow with their own car following logic.

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Testing Collision Avoidance Mode

• Worst-case stopping is used to show the effectiveness of our collision avoidance mode for ACC/CACC vehicles.

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ACC Performance with Different Time gap setting

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CACC Performance with Different Time gap setting

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Smoothing Traffic Flow Using ACC/CACC

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Sensor Measurement Error

• The accuracy of sensor measurement is important and can have significant impact on overall vehicle stream performance.

• Measurement accuracy of a typical radar sensor for ACC is considered as random percentage error in our simulation. A ±5% error in relative speed measurement and ±1% error in gap measurement are captured in ACC/CACC car following model.

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CACC Stability with Lossy Links

• When packet loss occurs in imperfect wireless communication, previously stored acceleration information will be used to determine the desired acceleration.

• We define Packet Loss Ratio (PLR) as the ratio of beacon messages that are dropped in each simulation time-step in each vehicle.

• beacon messages are dropped in application layer with uniform distribution.

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Expected throughput with vehicle platooning

0 5 10 15 20 250

1000

2000

3000

4000

5000

Throughput

Tg=0.55 sTg=0.60 sTg=0.65 sTg=0.70 s

Platoon Size N

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Throughput of CACC platooning with different platoon size and intra-platoon time gap setting

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Conclusion1) Through our analytical model, we demonstrate how time gap setting,

design parameters and imperfect communication can affect local and string stability of ACC and CACC vehicles.

2) ACC and CACC vehicle performance in worst-case stopping scenario and with different time gap setting as well as following a real stop-and-go traffic are simulated and results confirm that ACC and CACC vehicles can increase highway throughput and improve traffic stability.

3) Simulation with measurement error and imperfect wireless communication shows the stability and robustness of ACC and CACC control system. Wireless communication, even with high packet loss ratio, can be beneficial and ensure better CACC vehicle performance than ACC vehicles.

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• Related publication:Hui Deng, Mani Amoozadeh, H. Michael Zhang, Chen-Nee Chuah, and Dipak Ghosal, “Local and String Stability of Vehicle Streams with Enhanced ACC/CACC Control”, submitted to Intelligent Transportation Systems, IEEE Transactions on, 2014

Thank you!

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Platooning management

• The idea of organizing traffic in platoons to dramatically increase capacity is originally proposed by PATH for Intelligent Vehicle Highway System (IVHS)[7].

• With wireless communication, CACC vehicles can group into tight platoons. Collaborative driving system can be formed with CACC vehicles to improve highway safety and efficiency.

• As vehicles have small spacing and same speed within platoon, air resistance force can be highly reduced and leads to reduced fuel consumption and emissions.

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Platoon Merge Maneuver

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Platoon Split Maneuver

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Selected Reference1) G. Marsden, M. McDonald, and M. Brackstone, “Towards an understanding of adaptive cruise control,”

Transportation Research Part C: Emerging Technologies, vol. 9, no. 1, pp. 33–51, 2001.2) F. Bu, H.-S. Tan, and J. Huang, “Design and field testing of a cooperative adaptive cruise control system,” in

American Control Conference (ACC), 2010. IEEE, 2010, pp. 4616–4621.3) B. Van Arem, C. J. van Driel, and R. Visser, “The impact of cooperative adaptive cruise control on traffic-

flow characteristics,” Intelligent Transportation Systems, IEEE Transactions on, vol. 7, no. 4, pp. 429–436, 2006.

4) S. E. Shladover, D. Su, and X.-Y. Lu, “Impacts of cooperative adaptive cruise control on freeway traffic flow,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2324, no. 1, pp. 63–70, 2012.

5) C. Lei, E. van Eenennaam, W. K. Wolterink, G. Karagiannis, G. Heijenk, and J. Ploeg, “Impact of packet loss on cacc string stability performance,” in ITS Telecommunications (ITST), 2011 11th International Conference on. IEEE, 2011, pp. 381–386.

6) V. Milan´es, S. E. Shladover, J. Spring, C. Nowakowski, H. Kawazoe, and M. Nakamura, “Cooperative adaptive cruise control in real traffic situations,” 2014.

7) P. Varaiya, “Smart cars on smart roads: problems of control,” Automatic Control, IEEE Transactions on, vol. 38, no. 2, pp. 195–207, 1993.

8) Robinson, Tom, Eric Chan, and Erik Coelingh. "Operating platoons on public motorways: an introduction to the SARTRE platooning programme."Proceedings of the 17th ITS World Congress, Busan. 2010.

9) T. Robinson, E. Chan, and E. Coelingh, “Operating platoons on public motorways: an introduction to the sartre platooning programme,” in 17th World Congress on Intelligent Transport Systems, 2010, pp. 1–11.