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

2 Challenge the future 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 Challenge the future 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 Challenge the future 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 Challenge the future 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 Challenge the future Worked examples LayoutObjectivesFeature 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 Anticipation of leader behaviour Eco-driving concept C-ACC in homogeneous platoon Maximise safety, efficiency and comfort for all cooperative vehicles Anticipation of leader behaviour Exchange predicted state and control information C-ACC in mixed platoonMaximise safety, efficiency and comfort for the cooperative vehicle and its follower(s) Anticipation of leader behaviour Prediction of follower behaviour, using imperfect car- following model No V2V communication needed

7 Challenge the future 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 Challenge the future ACC string stability regions Homogeneous traffic flow stability 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 S: Stable CU: Convective upstream instability A: Absolute instability CD: Convective downstream instability Driving direction Speed (km/h)

9 Challenge the future 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 Challenge the future Impacts of ACC on moving jams 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). Driving direction Flow (veh/h) Speed (km/h) Flow (veh/h) Speed (km/h) Flow (veh/h) Speed (km/h)

11 Challenge the future Impacts of C-ACC 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). Driving direction Flow (veh/h) Speed (km/h) Flow (veh/h) Speed (km/h) Flow (veh/h) Speed (km/h)

12 Challenge the future Connected traffic control and vehicle control Scenarios # of detected jams # of resolved jams TTS (veh·h) Speed limits area (km·min) 0% ACC without VSL % ACC without VSL % ACC without VSL % ACC with VSL % ACC with VSL % ACC with VSL 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). VSL: Variable Speed Limits TTS: Total time spent in the network

13 Challenge the future 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 Challenge the future 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. Submitted to 2016 Annual Meeting of Transportation Research Board (TRB). 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 Challenge the future Meng Wang Thank you!