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C/ACC controller in Aimsun Next

Technical Note #63

By Martin Hartmann

October 2021

Intro

Vehicle automation attracts stakeholders that seek to predict the impacts of this technology on road traffic. The diverse stakeholder perspectives might range from high-level traffic planning policies to operational aspects for validation of perception sensors under adverse weather conditions. Aimsun covers the entire domain spectrum by offering modeling tools and services, from macroscopic models to complex real-time co-simulations of AV stacks with digital twins representing realistic traffic environments.

In this article, we explain and demonstrate the use of microscopic simulation of vehicles equipped with cooperative adaptive cruise control (CACC) in Aimsun Next. Since version 8.4, Aimsun Next has included a native implementation of the ACC and CACC algorithm (here labelled as C/ACC) developed by Milanes & Shladover, 2014 [1]. Hence, this technical note sets the controller implementation in the context of a microsimulation model, describes the parameter revealed to the user, and gives some practical examples from using the controller.

 

Key take-aways

C/ACC vehicles exhibit car-following behavior significantly different from the conventional (in Aimsun Next: Gipps-controlled) vehicles allowing very short time gaps within the platoons of connected vehicles. Let’s summarize the most important information about the C/ACC algorithm in context of microsimulation in Aimsun Next:

  • Vehicles can be equipped either with ACC, CACC or non-equipped.
Figure 1: The C/ACC distribution is defined in each vehicle type editor
  • C/ACC algorithm overwrites the default longitudinal behavior (acceleration and speed of the Gipps car-following model).

 

  • The reaction time must be set to 0.1s for C/ACC equipped vehicles, conventional vehicles are recommended to keep their own reaction time (around 0.8s).
Figure 2: The RT distribution is defined under the Experiment settings
  • Any vehicle equipped with CACC is capable of ACC. Therefore, CACC equipped vehicle following a vehicle without CACC is using ACC controller “only”.

 

  • Additional CAMP collision avoidance algorithm keeps safe car-following gaps.

 

  • The possibility for a vehicle to activate C/ACC and the maximum platoon size can be set in the road type.
  • The resulting platooning behavior is affected by the combination of vehicles’ desired speed, max. platoon size and C/ACC controller parameters.

Controller states description

Next, there are five states that apply to a CACC-equipped vehicle:

  1. CC Speed regulation (“no preceding vehicle in your detection range, choose your acceleration”)
  2. ACC Gap regulation (“follow a preceding vehicle*, adjust acceleration to meet the desired time gap”)
  3. CACC Platoon Leader Gap Regulation (“max platoon size reached, you are the new leader, choose your acceleration”)
  4. CACC Platoon Follower Gap Regulation (“follow connected leader, adjust acceleration to meet the desired intra-platoon time gap”)
  5. Disabled (“no controller equipped or temporary disabled by CAMP algorithm”)
Figure 3: The state diagram and the conditions describing the state transitions

* the preceding vehicle can be a non-equipped vehicle, ACC or CACC equipped vehicle within the ACC Lower clearance threshold or CACC Lower Gap Threshold

 

ACC platoon parameters

  • ACC controller gains: sensitivity parameters of the ACC controller on both the positioning and speed errors during free flow and following. The values are set to default values from the [1] paper and has been found to minimize the difference between the real empirical and simulated results. We recommend to most of the users to take over the default values.
  • Lower clearance threshold: on- board detectors of the subject vehicle identified a leader; the subject vehicle enters the ACC Gap regulation state and tries to reach the Desired Time Gap.
  • Upper clearance threshold: the leader is beyond the on-board sensors’ detection range; the subject vehicle leaves the platoon and enters the CC Speed regulation
  • Desired Time Gap: the time distance between the subject vehicle and the leader within the platoon, the default value is set to the Nowakovski distribution (1,2 / 0,4 / 1,1 / 2,2s) [3].

CACC platoon parameters

  • CACC controller gains: sensitivity parameters of the CACC controller adjusting the time gap between the subject vehicle and the leader.
  • Current time gap > Upper Gap Threshold: the subject vehicle switches to the ACC Gap regulation state (e.g. the leader has increased its desired speed and is beyond the connectivity range).
  • Current time gap < Lower Gap Threshold: the subject vehicle switches to the CACC Platoon Follower Gap Regulation state aiming for the intra-platoon Time Gap Follower (default 0,6s).
  • Current time gap < Upper Gap Threshold & Max platoon size is reached ® the subject vehicle become a leader of its own platoon and enters the CACC Platoon Leader Gap Regulation state aiming for the Time Gap Leader (default 1,5s).
  • Time Gap between Lower & Upper Gap Threshold: the subject vehicle will use the hysteresis control rule and apply the car-following state from the previous time step.

CAMP emergency takeover

The C/ACC model in Aimsun Next employs the CAMP forward collision warning algorithm [2]. This algorithm is included in the model to check if the gap between the subject vehicle and the leader is sufficient for safe car-following. If the CAMP algorithm is activated in any point, the C/ACC controller is disabled followed by a 20s cooldown before re-entering the last C/ACC state again.

 

Simulation examples

  1. CACC equipped vehicle (in red) with no preceding vehicle ® CC speed regulation
  1. CACC equipped vehicle (red, desired speed = 49,9 km/h) follows a non-equipped vehicle (blue, desired speed = 57 km/h) ® ACC speed regulation until Clearance > Upper clearance threshold (120m) ® CC speed regulation

Slow down the recording and highlight the Clearance and the Cruise Control Status values

 

Comment: You can collect individual vehicles’ time series (TS) of both values Gap to the leader (blue line) and Clearance to the leader (green line) to visualize the behavior.

  1. CACC equipped vehicle is connected and part of the platoon aiming for the intra-platoon Time Gap 0,6s > CACC Platoon Follower Gap Regulation
  1. CACC equipped vehicle with no leader (CC Speed regulation active) shortly before identifying a new preceding vehicle. After the Clearance drops below the Lower Clearance Threshold of 100m, the subject vehicle changes its state into the ACC Gap regulation.

Slow down the recording and highlight the Clearance and the Cruise Control Status values

 
  1. CACC equipped vehicle shortly before entering the CACC platoon: Current Time gap to go below Lower Gap Threshold of 0,5s applying the ACC Gap regulation for the moment (left image) and after entering the CACC platoon aiming for the Time Gap Follower ® CACC Platoon Follower Gap Regulation (right image).

Slow down the recording and highlight the Gap and the Cruise Control Status values

 
  1. platoon size restriction forces the subject vehicle to become a new platoon leader (left-most CACC platoon). The leader of the right-most platoon is in the ACC Gap regulation state as the preceding vehicles is closer than the Upper Clearance Threshold of 120m. After exceeding this threshold, the leader enters the CC Speed regulation state.

Closing remark

In the implemented model, a true platoon leader can be labelled with different CACC Control Status tags (even with platoon size = 1):

  • CACC Platoon Leader: if the Max Platoon Size forces the vehicle to create a new platoon,
  • ACC Gap Regulation: if the platoon leader is within the sensor range to a preceding vehicle,
  • CC Speed Regulation: if the platoon leader is outside of the sensor range to a preceding vehicle.

 

References

[1] Vicente Milanés, Steven E. Shladover. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation research. Part C, Emerging technologies, Elsevier, 2014, pp.285-300. 10.1016/j.trc.2014.09.001 . hal-01091160

 

[2] Kiefer, R.J., Cassar, M.T., Flannagan, C.A., LeBlanc, D.J., Palmer, M.D., Deering, R.K.,    Shulman, M.A., 2003. Forward  collision  warning  requirements  project:  refining the  CAMP  crash  alert  timing  approach  by  examining”  last-second”  braking  and  lane change  maneuvers  under  various  kinematic  conditions.NHTSA  Research  Report HS-809 574.

 

[3] Nowakowski,  C.,  J.  O’Connell,  S.E.  Shladover,  and  D.  Cody,  2010,  “Cooperative Adaptive  Cruise  Control:  Driver  Selection  of  Car-Following    Gap  Settings  Less  Than One  Second”,  54th  Annual  Human  Factors  and  Ergonomics  Society  Meeting,  San Francisco, CA.

More technical notes

Side-lane merging in an Aimsun Next mesoscopic model

June 2022: Aimsun Next has a specific mesoscopic merging behavior model that is controlled by the cooperation gap and merging gap parameters. Mohammad Saifuzzaman explains how to use these parameters to control merging behavior and match observed traffic flow and congestion.

Hybrid Simulation

February 2018: Geline Canayon and Laura Torres give you a brief guided tour through the how, when, where and why of hybrid simulation.

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Aimsun Next 23

Aimsun (2023). Aimsun Next 23 User's Manual, Aimsun Next Version 23.0.0, Barcelona, Spain. Accessed on: July. 19, 2023. [Online].
Available: https://docs.aimsun.com/next/23.0.0/

 

 

Aimsun Next 20.0.5

Aimsun (2021). Aimsun Next 20.0.5 User's Manual, Aimsun Next Version 20.0.3, Barcelona, Spain. Accessed on: May. 1, 2021. [In software].
Available: qthelp://aimsun.com.aimsun.20.0/doc/UsersManual/Intro.html
 

Aimsun Next 23

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edition =  {​​​​​​​​​​​​​​​Aimsun Next 23.0.0}​​​​​​​​​​​​​​​,
address = {​​​​​​​​​​​​​​​Barcelona, Spain}​​​​​​​​​​​​​​​,
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Aimsun Next 20.0.5

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Aimsun Next 23

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Y2 - Accessed on: Month, Day, Year
CY  - Barcelona, Spain
PB  - Aimsun
UR  - [In software]. Available: https://docs.aimsun.com/next/23.0.0/

Aimsun Next 20.0.5

TY  - COMP
T1  - Aimsun Next 20.0.5 User's Manual
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