5. Combined Longitudinal and Lateral Control Design
6.3 Urban-road Testing
(a) Low speed CC mode. (b) Stop-and-go mode.
Fig. 6-14. The in-vehicle view for stop-and-go maneuver in the Nanliao Harbor Park.
In Fig. 6-14, the in-vehicle view represents typical traffic conditions at low speed in urban-road environment. Figure 6-14(a) represents that no preceding-vehicle in the front of the subject-vehicle and the system activates CC mode initially. The subject-vehicle will keep the reference velocity which can be chosen by the driver or the maximum velocity of urban-road. If a preceding-vehicle with lower speed appears in the same lane and finally stops ahead, the subject-vehicle reduces its speed to approach the preceding-vehicle gradually until it comes to a halt at a safe distance behind the preceding one. When the preceding-vehicle restarts moving, the subject-vehicle also moves and keeps a safe headway distance at any speed. Figure 6-14(b) represents the typical traffic jam scenario in which the vehicle continuously stopping and starting. The system adapts the subject-vehicle’s speed to follow the preceding vehicle which is driven manually, stopping when necessary and keeping a safe distance even when circulating at low speeds.
As shown in Fig. 6-15, the initial condition of this scenario is that both the preceding- and subject-vehicles are stationary in the same lane with over 50 m apart and facing in the same direction. Here the maximum speed of CC mode is set as 30 km/h, 2 s for the headway time, and 4 m for the minimum stopping distance which is the typical length of a vehicle. The subject-vehicle starts driving along its lane at 5 s, with initial CC mode. As the subject-vehicle approaches to a stopping preceding-vehicle, the headway distance decreases, and the system switches to the stop-and-go mode, adjusting the speed to keep a safe distance.
As the headway distance decreases, the throttle degree is also gradually released. Around 28 s,
the headway distance begins to be smaller than the safe distance, and thus the desired speed is less than the vehicle speed. At this point, the system applies the brake to slow down the vehicle, and the vehicle continues reducing speed until its headway distance reaches the minimum stopping distance. The second graph shows that the subject-vehicle stops at 4 m behind the preceding-vehicle from 32 s to 38 s. The preceding-vehicle then starts moving, followed by the subject-vehicle. This throttle/brake control behavior is very similar to human driving: The driver accelerates until a front car appears in the driving lane, then releases the degree on the throttle to slightly reduce speed and, if this reduction is not enough, applies the brake until the car stops without collision to the front car. This experiment reproduces the case of typical stop-and-go situation: the preceding vehicle is stationary and the following vehicle approaches the preceding one at high speed; this situation is very common at the tail end of a traffic jam and causes a lot of rear-end crashes.
0 10 20 30 40 50 60
Fig. 6-15. Low speed CC mode switches to stop-and-go mode.
0 10 20 30 40 50 60 70 80 90
Fig. 6-16. Stop-and-go mode of the supreme 30 km/h.
This scenario is the realization of a stop-and-go automatic car-following. Figure 6-16 indicates the reaction of the controlled vehicle when the preceding one carries out successive stopping and restarting. The controlled vehicle tracks the preceding one very accurately and respects the headway targets, 2 s for headway time, and 4 m for minimum headway distance by using both pedals when necessary. As shown in the top figure, the controlled vehicle stops at 4 m behind the preceding vehicle at 27 s, 54 s and 75 s. While the preceding vehicle restarts, the controlled vehicle always tracks the safe distance to move. This experiment reflects the situation that the controlled vehicle tracks preceding one which stops and starts alternatively, as in a traffic jam. As speeds and distances are low, the rear-end crash risk decreases, but these situations are boring and tedious for human drivers and accidents are very common. The automation of these maneuvers is consequently one objective of the intelligent transportation systems.
In the Nanliao Harbor Park, the map information is initially obtained. Thus lane-change control based on the vision system and RTK-DGPS can be compared. In the following results, the term GPS offset (in cm unit) refers to the lateral offset which is recorded by RTK-DGPS
for the free lane-change maneuver. As shown in Fig. 6-17, the system can achieve the request of 3 meter offset for free lane-change maneuver. Though the vision-based lane-change is in the form of open-loop control, the performance is comparable to the GPS-guided lane-change which is shown in Fig. 6-18. Consequently, LC mode can be operated either using the vision system or GPS-guided map regarding to the driving circumstance.
Fig. 6-17. Experimental results of the vision-based lane-change maneuver.
Fig. 6-18. Experimental results of the GPS-guided lane-change maneuver.
Chapter 7
Conclusions and Future Works
An automated driving control system with multi-mode operation is proposed. The system is constructed in a hierarchical autonomy structure to achieve integrated longitudinal and lateral motion control. Upper-level control determines the driving mode: the reference velocity is calculated by an adapted SMC technique with simplicity and robustness properties; the reference trajectory is generated by the ride comfort constraint. Vehicle-body control is designed on the basis of the fuzzy control technique for managing the vehicle’s throttle-, brake- and SW-motor in such way to mimic a human driver. By means of the MABX or DSP board, and self-installed sensors and actuators, this system is implemented on an experimental vehicle, Taiwan iTS-1. The experimental results in real-traffic environments show that Taiwan iTS-1 not only performs well in expressway/highway for CC, ACC, LK, and LC mode, recpectively, but also extends the low speed capability in the urban-road for stop-and-go mode. The implemented system is designed separately with regard to the vehicle longitudinal and lateral dynamics. Besides, to handle the coupling characteristics, a fuzzy systematic design of the combined control of vehicle longitudinal and lateral motions is also presented. Several conventional simplifications or assumptions on vehicle dynamics are not used to access the applicable region of driving conditions. Numerical simulations show the feasibility of our proposed control algorithm and its superior performance in comparison with other approaches.
In the future, we shall conduct more complex situations such as overtaking which can be utilized to dodge the obstacles or overtake a slow-moving vehicle ahead. More sensory systems (e.g., GPS, v-v communication) and sensor-fusion techniques will be more considered in our future design. Besides, for various road conditions in these complex traffic situations, the robustness of control design arises in the variation of tire/ground characteristics (e.g. slip friction on road surface). Furthermore, we shall integrate throttle, brake, and SW to achieve automated driving in urban roads, where more maneuvers are needed to make our driving system available for various driving situations. The adaptive scheme for handling different driving situations is required to be considered in the vehicle-body control design, e.g.
adaptive controlling gain maneuver in speed regulation control. A novel spacing policy,
especially for stop-and-go mode, should be developed to grasp the realistic behavior of human drivers such as keeping tightly with the preceding vehicle. In order to achieve the more intact and omnibus automated driving system, the stop-and-go maneuver with extension to the assistance of intersection issue and related work will be our future research works.
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Appendix A
The specification of Taiwan iTS-1 and equipments are listed in the following tables:
TABLE A.1 Specification of Taiwan iTS-1.
Mitsubishi SAVRIN 2.4
Vehicle width 1.78 m
Vehicle length 4.70 m
Weight 1640 ~ 2200 kg
Engine type L4 DOHC 16V VVT+DMM
Exhaust 2.4 L
Maximum Horsepower 150/6250 (HP/RPM) Maximum Torsion 19.2/3000 (KGM/RPM)
Transmission INVECS-II SPORTS-MODE 4 A/T
Powertrain Front-wheel drive
Tire 205/65R15
TABLE A.2 List equipments in Taiwan iTS-1.
Data Acquiring Sensors
CCD Monochromatic CCD
Range finder Laser range finder (LMS291)
GPS RTK-DPGS Signal Processors
In-vehicle controller MABX – dSPACE or DSP F2812 Image processor DM642 – 600 M Hz
GPS processor DSP C6713DSK
Perception Sensors
Yaw rate sensor IMU300CC
Accelerometer IMU300CC
Velocity Vehicle speedometer
Actuators
SW motor AC motor, MSMA042A1E by panasonic
Throttle motor DC motor, AP500 by Liteon
Brake motor DC motor, linear actuator by HIWIN Corp.
The nominal values of parameters for Taiwan iTS-1 is list in the following table:
TABLE A.3 Nominal parameter values of the vehicle platform (Taiwan iTS-1) Symbol and description Nominal value
m Mass of the vehicle 1640 ~ 2200 kg
Iz Yaw moment of inertia 2300 kg*m2
a Distance from CG to the front-axle 1.193 m b Distance from CG to the rear-axle 1.587 m
Lwb Length of wheelbase 2.78 m
Cf Total cornering stiffness of the front-tire 131391 Nt/rad Cr Total cornering stiffness of the rear-tire 115669 Nt/rad
f Rotating friction coefficient 0.02
kD Drag coefficient from aerodynamics 0.41 Nt*s2/m2 kL Lift coefficient from aerodynamics 0.005 Nt*s2/m2
Appendix B: ISO 15622
Fig. B.1. Horizontal detection area.
As a minimum requirement the detection and ranging shall cover at least the closest forward vehicle on the assumed trajectory on straight and in the constant radius part of curves.
ISO 15622 specifies that the required length of the considered trajectory depends on the vehicle speed, which itself is limited by the maximum speed and lateral acceleration constraints.
Limitation of speed in curves to a maximum lateral acceleration:
max max lateral _ max
Appendix C
The mathematical from of the Gipps model is shown as
( )
where parameters are listed as follows:
an Driver’s maximum allowable acceleration, vehicle n;
bn Driver’s maximum allowable deceleration, vehicle n;
ˆb Estimated value for bn-1 ;
sn-1 The effective size of vehicle n-1;
Xn(t) The location of the vehicle n at time t;
Vn(t) The velocity of vehicle n at time t;
Vno Driver’s desired velocity, vehicle n;
ε The apparent reaction time.
Vita
Name: Hsin-Han Chiang
Personal:
Place of Birth: Kaohsiung, Taiwan, R.O.C. Day of Birth: Oct. 1, 1979
Education:
Degree Date School
B.S. E.C.E 1997 – 2001 National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
M.S. E.C.E 2001 – 2003 National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
Ph.D. E.C.E 2003 – 2007 National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
Advisor(s):
M.S. -
Professor Tsu-Tian Lee, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
Ph.D. -
Professor Bing-Fei Wu, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
Professor Tsu-Tian Lee, National Taipei University of Technology, Taipei, R.O.C.
Publication List
Accepted Journal Papers:
[1] S. J. Wu, H. H. Chiang, H. T. Lin, and T. T. Lee, “Neural-network-based optimal fuzzy controller design for nonlinear systems,” Fuzzy Sets and Systems, Vol. 154, pp. 182-207, 2005.
[2] D. C. Liaw, H. H. Chiang, and T. T. Lee, “Elucidating vehicle lateral dynamics using a bifurcation analysis”, IEEE Trans. Intelligent Transportation Systems, Vol. 8, No. 2, pp.
195-207, 2007.
[3] B. F. Wu, C. J. Chen, H. H. Chiang, H. Y. Peng, J. W. Perng, L. S. Ma, and T. T. Lee,
“The design of intelligent real-time autonomous vehicle, Taiwan iTS-1”, Journal of the Chinese Institute of Engineerings, Vol. 30, No. 5, pp. 829-842, 2007.
[4] H. H. Chiang, S. J. Wu, J. W. Perng, B. F. Wu, and T. T. Lee, “The human-in-the-loop design approach to the longitudinal automation system for an intelligent vehicle”, accepted to be published in IEEE Trans. Systems, Man, and Cybernetics-Part A:
Systems and Humans, Aug., 2007.
[5] S. J. Wu, H. H. Chiang, J. W. Perng, C. J. Chen, B. F. Wu, and T. T. Lee, “The heterogeneous systems integration design and implementation for lane-keeping on a vehicle”, accepted to be published in IEEE Trans. Intelligent Transportation Systems, Oct., 2007.
Submitted Journal Papers:
[1] S. J. Wu, C. T. Wu, H. H. Chiang, S. S. Yu, T. T. Lee, “Neural-fuzzy control design for current-controlled magnetic bearings with minimum power,” submitted to IEEE Trans.
Neural Networks, Oct. 2007.
[2] S. J. Wu, H. H. Chiang, J. W. Perng, B. F. Wu, and T. T. Lee, “Toward driver-free automated vehicle control systems on Taiwan iTS-1,” submitted to IEEE/ASME Trans.
Mechatronics, Nov. 2007.
International Conference Papers:
[1] H. H. Chiang, S. J. Wu, and T. T. Lee, “Application of fuzzy sets theory in process data filtering”, in Proc. IEEE International Conference on Fuzzy systems, pp. 325-330, 2003, Minnesota, USA.
[2] H. H. Chiang, S. J. Wu, and T. T. Lee, “Fuzzy approach for disturbance reduction”, in Proc. IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA’03), pp. 1416-1420, 2003, Kobe, Japan.
[3] S. J. Wu, H. H. Chiang, J. H. Chen, and T. T. Lee, “Optimal fuzzy control design for half-car active suspension systems”, in Proc. IEEE International Conference on Networking, Sensing, and Control (ICNSC’04), pp. 583-588, 2004, Taipei, Taiwan.
[3] S. J. Wu, H. H. Chiang, J. H. Chen, and T. T. Lee, “Optimal fuzzy control design for half-car active suspension systems”, in Proc. IEEE International Conference on Networking, Sensing, and Control (ICNSC’04), pp. 583-588, 2004, Taipei, Taiwan.