1. Controller design for autonomous and cooperative driving and impact assessment on traffic flow dynamics
Generic Model Predictive Control Framework for Advanced Driver Assistance Systems (ADAS)
Meng Wang
Department of Transport & Planning
Department of BioMechanical Engineering
Supervisor(s): Winnie Daamen, Serge Hoogendoorn, Bart van Arem

2. Advanced Driver Assistance Systems
Support drivers in performing driving tasks in (partially) automated vehicles
Autonomous systems, e.g. Adaptive Cruise Control (ACC)
Rely solely on on-board sensors
No cooperation in the decision-making
Cooperative systems, e.g. Cooperative ACC (CACC)
Exchange information via V2V/V2I communication
Coordination and consensus in decision-making

3. Relevant for traffic management?
ADAS may have far-reaching impacts on:
Individual driver behaviour: car-following and lane-changing, consequently travel time, safety and comfort
Collective traffic flow characteristics: capacity, stability
Sustainability: fuel consumption and emissions
It is important to design ADAS to improve collective traffic flow dynamics!

4. A flexible design approach
Motivation: many control approaches determine ACC/C-ACC accelerations based on simple linear feedback control law
Approaches often miss certain desirable features, such as:
Explicit optimisation
Multiple objectives
Anticipation on (future) driving context
Integration with current traffic management architecture (V2I)
Goal: to develop a generic multi-objective control approach based on MPC (Model Predictive Control), while being fast and robust enough for real-time application

5. Predicting dynamic behaviour of: controlled vehicles
surrounding vehicle(s) using human behaviour models
Autonomous/non-cooperative: optimisation of own cost
Cooperative system: joint optimisation of total costs
Acceleration
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Rolling horizon control framework for driver assistance systems. Part I: Mathematical formulation and non-cooperative systems. Transportation Research Part C, 2014,40, pp
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control. Transportation Research Part C, 2014,40, pp

6. Worked examples Layout Objectives Feature ACC
(1) Maximise safety by penalising approaching leader at small gaps
(2) Maximise efficiency by penalising deviation from desired speed/gap
(3) Maximise comfort by penalising large accelerations and braking
Anticipation of leader behaviour
Full speed range
EcoACC
Basic ACC objectives +
Minimise fuel consumption and emissions
Eco-driving concept
C-ACC in homogeneous platoon
Maximise safety, efficiency and comfort for all cooperative vehicles
Exchange predicted state and control information
C-ACC in mixed platoon
Maximise safety, efficiency and comfort for the cooperative vehicle and its follower(s)
Prediction of follower behaviour, using imperfect car-following model
No V2V communication needed

7. Traffic flow fundamental diagram
ACC (Efficient-driving) v.s. EcoACC (Eco-driving)
Single lane simulation homogeneous vehicles
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Potential impacts of ecological adaptive cruise control systems on traffic and environment. IET Intelligent Transport Systems, 2014, 8, pp

8. Homogeneous traffic flow stability
Speed (km/h)
Driving direction
ACC string stability regions
Speed (km/h)
Driving direction
S: Stable
CU: Convective upstream instability
A: Absolute instability
CD: Convective downstream instability
M. Wang, M. Treiber, W. Daamen, S.P. Hoogendoorn, B. van Arem. Modelling supported driving as an optimal control cycle: Framework and model characteristics. Transportation Research Part C, 2013, 36, pp

9. Mixed traffic flow features
2-lane motorway of 14 km, more than 500 vehicles
Complex networked control problem: distributed MPC algorithm
Temporary bottleneck by lowering speed limits to 50 km/h
Mixed human-driven and ACC vehicles
Mixed human-driven and C-ACC vehicles
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Cooperative car-following control: distributed algorithm and impact on moving jam features. IEEE transactions on ITS, 2015 (under review).

10. Impacts of ACC on moving jams
Flow (veh/h)
Flow (veh/h)
Flow (veh/h)
Driving direction
Speed (km/h)
Speed (km/h)
Speed (km/h)
Driving direction
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Cooperative car-following control: distributed algorithm and impact on moving jam features. IEEE transactions on ITS, 2015 (under review).

11. Impacts of C-ACC Driving direction Driving direction
Flow (veh/h)
Flow (veh/h)
Flow (veh/h)
Driving direction
Speed (km/h)
Speed (km/h)
Speed (km/h)
Driving direction
M. Wang, W. Daamen, S.P. Hoogendoorn, B. van Arem. Cooperative car-following control: distributed algorithm and impact on moving jam features. IEEE transactions on ITS, 2015 (under review).

12. Connected traffic control and vehicle control
VSL: Variable Speed Limits
TTS: Total time spent in the network
Scenarios
# of detected jams
# of resolved jams
TTS (veh·h)
Speed limits area (km·min)
0% ACC without VSL -
562.8
10% ACC without VSL
453.8
100% ACC without VSL
443.8
0% ACC with VSL 10
475.0
75.2
10% ACC with VSL
439.2
73.5
100% ACC with VSL 12
441.7
59.9
M. Wang et al. Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves. Journal of ITS, 2015 (under review).

13. Summary
A generic control design methodology for a variety of ADAS applications
Implementable algorithms for ACC and C-ACC controllers
Impacts of ACC and C-ACC systems on flow characteristics are substantial, particularly in formation and propagation properties of moving jams
Proposed ACC and C-ACC systems mitigate congestion compared to human- driven vehicles
Connected variable speed limits control with ACC brings extra benefits

14. Still challenging… Delay and inaccuracy in the loop
M. Wang, S.P, Hoogendoorn, W. Daamen, B. van Arem, B. Shyrokau, and R. Happee. Delay-compensating strategy to enhance string stability of autonomous vehicle platoons. Transportmetrica B (accepted).
Cooperative merging and lane changing control
M. Wang, S.P, Hoogendoorn, W. Daamen, B. van Arem, and R. Happee. Game theoretic approach for predictive lane-changing and car-following control. Transportation Research Part C, 2015, 58, pp
Human factors, driver’s role in the future:
Supervising, resume control, safety concern?
Impact assessment
Are microscopic traffic simulation models capable for the job?
Cooperative traffic management
Refine or redesign current traffic management systems?

15. Meng Wang m.wang@tudelft.nl www.mengwang.eu
Thank you!
Meng Wang