1. Driving Assist System for Ecological Driving Using Model Predictive Control Presented by ー M.A.S. Kamal - Fukuoka IST Co-Authors ー Maskazu Mukai - Kyushu University Junichi Murata - Kyushu University Taketoshi Kawabe - Kyushu University SICE 9 th Annual Conference on Control System Hiroshima, March 05, 2009

2. 2 Outline  Ecological Driving  Needs of an Assist System  Modeling of the System  Controlling a Vehicle  Simulation Results  Conclusions and Future Work

3. 3 Ecological Driving Scarce of Oil, Global warming, Environment pollutions force us to an idea of Eco-Driving. Eco Driving Aims in:  Increase in Mileage.  Reduce Emissions of CO 2.  Reduce noise pollution.  Reduce accidents.  Reduce impact on environment.  Environment Friendly System

4. 4 Realization of Eco-Driving By Proper  Vehicle maintenance,  Route Selections,  Traffic Signaling Systems,  Driving Style  Vehicle Control. A good Driving or Vehicle Control Style may save fuel consumption significantly.

5. 5 Eco-Vehicle Control Strategy Desired behavior includes:  Minimize acceleration and braking.  Smooth and higher acceleration at starting.  Cruise at the best economy speed.  Stop by coasting or little braking. Inevitable Situations:  At urban traffic or in traffic congestion.  A red signal.  Braking preceding vehicle.

6. 6 Remarkable feature  Eco Driving Tips based on Vehicle Engine fuel consumption characteristics.  Qualitative Assistance without rigorous reasoning: “do not accelerate very hard” Limitations:  No indication what should be the exact acceleration; no a quantitative value (e.g. 2.3 m/s 2 ).  No analysis of the traffic trends, which greatly influenced on acceleration/braking on urban traffic. Conventional Eco-Strategy Solutions ?

7. 7 Concept of the Proposed Eco-Strategy Control the Vehicle by Anticipating future situations. A Driving Assist System can help a driver to attain or refine his Eco-Driving Skills. “Slow-and-go is always better than stop-and-go” Driving Assist System:  Generation of optimal action using model predictive control.  Assistance to the driver through human interface.

8. 8 Problem Formulation ホス ト Scope and Assumptions  Single Lane.  Only the immediate Preceding Car.  Flat road, no slope.  Longitudinal Motion Control.  Preceding Vehicle will move at its current acceleration/deceleration.  A Dummy vehicle stopped at red signal.

9. 9 Problem Formulation H P１P１ HV position HV Speed PV position PV Speed Input ： Assumption ： time dependent variable, at t, remains constant for a while.

10. 10 Model Predictive Control 0 1 N u0u0 u N-1 u1u1 ∆u∆u The problem is discretized in N step Prediction control Optimize [u 0,u 1,…u N-1 ] T to minimize the cost: HV PV Model Predictive Vehicle Control x(t) u(t)  Model of Vehicle Control System  Performance Index  Optimization of Control Inputs Sensors

11. 11 Fuel Consumption Model A continuous function for approximation of fuel consumption is derived as:

12. 12 Performance Index Fuel Economy Safe Clearance Reference Speed* Dynamic weights w 1, w 2, w 3 focus their relative contextual merits.

13. 13 Optimization of control inputs Continuation method combined with Gneralized Minimum Residual Algorithm is used to derive the solution with a given initial value vector. Required condition in finding the optimum control inputs: The Hamiltonian Function is given by :

14. 14 Simulations  Prediction Horizon: T= 10 sec, N=10, and h=1.0.  Simulation step 0.1 sec.  Control input constraint: -2.75  u  2.75  Time headway in car following h d =1.3sec.  Parameters of a Ford Feista Car. Observation 1: Starting from Standstill with no Preceding Vehicle.  Highest initial acceleration.  Reaches full speed at about 10 sec.  Continues cruising at the best economy speed.

15. 15 Simulations Observation 2: A typical starting situation HV PV 13 m  Both Vehicle start from standstill.  HV Started with higher acceleration.  Control input is adjusted without any braking at closing range.

16. 16 Test Environment Test Environment Functions can be Extended through API Routine to control a car in a special way AIMSUNMicroscopic Traffic Simulator AIMSUN Microscopic Traffic Simulator

17. 17 Simulations Application Program Interface Routine Info of the Host and surrounding vehicles Control Interface Fix a car as Host Vehicle Model Predictive Control Interface Observation 3: Comparison with Gipps based method, a default control system in AIMSUN. Fuel consumption ml and Mileages km/l are monitored.

18. 18 Simulations 728 m 510 m 285 m Test Route 90 Vh/h 585 Vh/h 465 Vh/h 90 Vh/h  A two lane road section.  Lane changes are not controlled.  A car is forced to stop at the beginning.  Then it controlled by MPC and Gipps methods in separate run.

19. 19 Simulations   Average fuel consumption: Gipps : 66.35 ml; MPC: 59.96 ml. Savings :6.39 ml or 10.65%   Average Millage/ economy : Gipps : 9.84 km/l; MPC: 10.96 km/l. Improvement : 11.39% 728 m 510 m 285 m Test Route 90 Vh/h 585 Vh/h 465 Vh/h 90 Vh/h

20. 20 Conclusions  A novel concept of Eco-Driving Assist System using Model predictive control has been presented.  Vehicle control assistance is based on both Fuel consumption and traffic trends.  The algorithm has been tested in AIMSUN, a traffic simulator with pseudo-realistic environment.  In a single road section of 700m, about 11.39% improvement in Mileage.

21. 21 Future Work  Refinement of the system to cope additional situations such as:  Roads with up-down slopes.  With known signal timing a priori.  Experimenting on real road systems. Thank you