Abstract

The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). First, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real-time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.

References

1.
Papageorgiou
,
M.
,
Mountakis
,
K.-S.
,
Karafyllis
,
I.
,
Papamichail
,
I.
, and
Wang
,
Y.
,
2021
, “
Lane-Free Artificial-Fluid Concept for Vehicular Traffic
,”
Proc. IEEE
,
109
(
2
), pp.
114
121
.10.1109/JPROC.2020.3042681
2.
Sahil
,
Sood
,
S. K.
,
2021
, “
Smart Vehicular Traffic Management: An Edge Cloud Centric IoT Based Framework
,”
Internet Things
,
14
, p.
100140
.10.1016/j.iot.2019.100140
3.
Hubmann
,
C.
,
Schulz
,
J.
,
Becker
,
M.
,
Althoff
,
D.
, and
Stiller
,
C.
,
2018
, “
Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction
,”
IEEE Trans. Intell. Veh.
,
3
(
1
), pp.
5
17
.10.1109/TIV.2017.2788208
4.
Tang
,
X.
,
Huang
,
B.
,
Liu
,
T.
, and
Lin
,
X.
,
2022
, “
Highway Decision-Making and Motion Planning for Autonomous Driving Via Soft Actor-Critic
,”
IEEE Trans. Veh. Technol.
,
71
(
5
), pp.
4706
4717
.10.1109/TVT.2022.3151651
5.
Li
,
L.
,
Zhao
,
W.
,
Xu
,
C.
,
Wang
,
C.
,
Chen
,
Q.
, and
Dai
,
S.
,
2021
, “
Lane-Change Intention Inference Based on RNN for Autonomous Driving on Highways
,”
IEEE Trans. Veh. Technol.
,
70
(
6
), pp.
5499
5510
.10.1109/TVT.2021.3079263
6.
Liu
,
Z.
,
Yuan
,
X.
,
Huang
,
G.
,
Wang
,
Y.
, and
Zhang
,
X.
,
2021
, “
Two Potential Fields Fused Adaptive Path Planning System for Autonomous Vehicle Under Different Velocities
,”
ISA Trans.
,
112
, pp.
176
185
.10.1016/j.isatra.2020.12.015
7.
Li
,
P.
,
Pei
,
X.
,
Chen
,
Z.
,
Zhou
,
X.
, and
Xu
,
J.
,
2022
, “
Human-Like Motion Planning of Autonomous Vehicle Based on Probabilistic Trajectory Prediction
,”
Appl. Soft Comput.
,
118
, p.
108499
.10.1016/j.asoc.2022.108499
8.
Krell
,
E.
,
King
,
S. A.
, and
Carrillo
,
L. R. G.
,
2022
, “
Autonomous Surface Vehicle Energy-Efficient and Reward-Based Path Planning Using Particle Swarm Optimization and Visibility Graphs
,”
Appl. Ocean Res.
,
122
, p.
103125
.10.1016/j.apor.2022.103125
9.
Yue
,
M.
,
Fang
,
C.
,
Zhang
,
H.
, and
Shangguan
,
J.
,
2021
, “
Adaptive Authority Allocation-Based Driver-Automation Shared Control for Autonomous Vehicles
,”
Accident Anal. Prev.
,
160
, p.
106301
.10.1016/j.aap.2021.106301
10.
Li
,
Y.
,
Chen
,
H.
,
Er
,
M. J.
, and
Wang
,
X.
,
2011
, “
Coverage Path Planning for UAVs Based on Enhanced Exact Cellular Decomposition Method
,”
Mechatronics
,
21
(
5
), pp.
876
885
.10.1016/j.mechatronics.2010.10.009
11.
Blasi
,
L.
,
D'Amato
,
E.
,
Mattei
,
M.
, and
Notaro
,
I.
,
2020
, “
Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph
,”
Appl. Sci.
,
10
(
16
), p.
5613
.10.3390/app10165613
12.
Zhang
,
Q.
,
Wu
,
K.
, and
Shi
,
Y.
,
2020
, “
Route Planning and Power Management for PHEVs With Reinforcement Learning
,”
IEEE Trans. Veh. Technol.
,
69
(
5
), pp.
4751
4762
.10.1109/TVT.2020.2979623
13.
Khatib
,
O.
,
1986
, “
The Potential Field Approach and Operational Space Formulation in Robot Control
,”
Adaptive and Learning Systems: Theory and Applications
,
Springer
, Boston, MA, pp.
367
377
.
14.
Rasekhipour
,
Y.
,
Khajepour
,
A.
,
Chen
,
S.-K.
, and
Litkouhi
,
B.
,
2017
, “
A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles
,”
IEEE Trans. Intell. Transp. Syst.
,
18
(
5
), pp.
1255
1267
.10.1109/TITS.2016.2604240
15.
Ji
,
J.
,
Khajepour
,
A.
,
Melek
,
W. W.
, and
Huang
,
Y.
,
2017
, “
Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints
,”
IEEE Trans. Veh. Technol.
,
66
(
2
), pp.
952
964
.10.1109/TVT.2016.2555853
16.
Chen
,
H.
,
Guo
,
L.
,
Ding
,
H.
,
Li
,
Y.
, and
Gao
,
B.
,
2019
, “
Real-Time Predictive Cruise Control for Eco-Driving Taking Into Account Traffic Constraints
,”
IEEE Trans. Intell. Transp. Syst.
,
20
(
8
), pp.
2858
2868
.10.1109/TITS.2018.2868518
17.
Nayak
,
J. R.
,
Shaw
,
B.
,
Sahu
,
B. K.
, and
Naidu
,
K. A.
,
2022
, “
Application of Optimized Adaptive Crow Search Algorithm Based Two Degree of Freedom Optimal Fuzzy PID Controller for AGC System
,”
Eng. Sci. Technol., Int. J.
,
32
, p.
101061
.10.1016/j.jestch.2021.09.007
18.
Shangguan
,
J.
,
Yue
,
M.
,
Fang
,
C.
, and
Qi
,
H.
,
2022
, “
Robust Design Optimization of Component Parameters for Dmdeb Powertrain System Based on Taguchi Method
,”
ASME J. Dyn. Syst., Meas., Control
,
144
(
9
), p.
091003
.10.1115/1.4054751
19.
Jain
,
S.
, and
Hote
,
Y. V.
,
2023
, “
Predictive Generalized Active Disturbance Rejection Control for Fractional Order Systems With Time Delay
,”
ASME J. Dyn. Syst., Meas., Control
,
145
(
7
), p.
071003
.10.1115/1.4062512
20.
Qi
,
G.
,
Yue
,
M.
,
Shangguan
,
J.
,
Guo
,
L.
, and
Zhao
,
J.
,
2024
, “
Integrated Control Method for Path Tracking and Lateral Stability of Distributed Drive Electric Vehicles With Extended Kalman Filter–Based Tire Cornering Stiffness Estimation
,”
J. Vib. Control.
,
30
(
11–12
), pp.
2582
2595
.10.1177/10775463231181635
21.
Huang
,
Y.
,
Ding
,
H.
,
Zhang
,
Y.
,
Wang
,
H.
,
Cao
,
D.
,
Xu
,
N.
, and
Hu
,
C.
,
2020
, “
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach
,”
IEEE Trans. Ind. Electron.
,
67
(
2
), pp.
1376
1386
.10.1109/TIE.2019.2898599
22.
Batta
,
N. A.
, and
Doscher
,
D. P.
,
2023
, “
Model Predictive Control of a Multi-Mode Suspension System Using Preview Information and Weight Optimization
,”
ASME J. Dyn. Syst., Meas., Control
,
145
(
6
) p.
065001
.10.1115/1.4062286
23.
Xu
,
C.
,
Yue
,
M.
,
Shangguan
,
J.
, and
Xu
,
M.
,
2024
, “
Multi-Source Motion Constrained Model Predictive Control for Tractor-Trailer Trucks With Coupled Dynamics
,”
Proc. Inst. Mech. Eng., Part D: J. Automobile Eng.
,
238
(
12
), pp.
3760
3778
.10.1177/09544070231185803
24.
Noto
,
N.
,
Okuda
,
H.
,
Tazaki
,
Y.
, and
Suzuki
,
T.
,
2012
, “
Steering Assisting System for Obstacle Avoidance Based on Personalized Potential Field
,”
15th International IEEE Conference on Intelligent Transportation Systems
, Anchorage, AK, Sept. 16–19, pp.
1702
1707
.10.1109/ITSC.2012.6338628
25.
Yang
,
C.
,
Huang
,
D.
,
He
,
W.
, and
Cheng
,
L.
,
2021
, “
Neural Control of Robot Manipulators With Trajectory Tracking Constraints and Input Saturation
,”
IEEE Trans. Neural Networks Learn. Syst.
,
32
(
9
), pp.
4231
4242
.10.1109/TNNLS.2020.3017202
26.
Xu
,
S.
, and
Peng
,
H.
,
2020
, “
Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles
,”
IEEE Trans. Intell. Transp. Syst.
,
21
(
1
), pp.
48
58
.10.1109/TITS.2019.2892926
27.
Funke
,
J.
,
Brown
,
M.
,
Erlien
,
S. M.
, and
Gerdes
,
J. C.
,
2017
, “
Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios
,”
IEEE Trans. Control Syst. Technol.
,
25
(
4
), pp.
1204
1216
.10.1109/TCST.2016.2599783
28.
Yang
,
L.
,
Yue
,
M.
,
Liu
,
Y.
, and
Guo
,
L.
,
2020
, “
RBFNN Based Terminal Sliding Mode Adaptive Control for Electric Ground Vehicles After Tire Blowout on Expressway
,”
Appl. Soft Comput.
,
92
, p.
106304
.10.1016/j.asoc.2020.106304
29.
Yue
,
M.
,
Shangguan
,
J.
,
Guo
,
L.
, and
Zhao
,
J.
,
2023
, “
All-in-One Control Framework for Distributed Drive Electric Buses Path Tracking Subject to Uncertain Crosswind and Varied Passenger Mass
,”
IEEE Trans. Veh. Technol.
,
72
(
7
), pp.
8342
8353
.10.1109/TVT.2023.3244980
30.
Zhao
,
W.
,
Qin
,
X.
, and
Wang
,
C.
,
2018
, “
Yaw and Lateral Stability Control for Four-Wheel Steer-by-Wire System
,”
IEEE/ASME Trans. Mechatron.
,
23
(
6
), pp.
2628
2637
.10.1109/TMECH.2018.2812220
31.
Wang
,
C.
,
Heng
,
B.
, and
Zhao
,
W.
,
2020
, “
Yaw and Lateral Stability Control for Four-Wheel-Independent Steering and Four-Wheel-Independent Driving Electric Vehicle
,”
Proc. Inst. Mech. Eng., Part D: J. Automobile Eng.
,
234
(
2–3
), pp.
409
422
.10.1177/0954407019860614
32.
Ziegler
,
J.
, and
Stiller
,
C.
,
2010
, “
Fast Collision Checking for Intelligent Vehicle Motion Planning
,”
IEEE Intelligent Vehicles Symposium
, La Jolla, CA, June 21–24, pp.
518
522
.10.1109/IVS.2010.5547976
You do not currently have access to this content.