Research on Integrated Decision Control Algorithm for Autonomous Vehicles Under Multi-Task Hybrid Constraints in Intelligent Transportation Scenarios

2022 ◽  
Author(s):  
Zonghuan Guo ◽  
Dihua Sun ◽  
Lin Zhou
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Process Analysis ◽  
Autonomous Driving ◽  
Test Method ◽  
Control Effect ◽  
Simulation Test ◽  
Intelligent Decision Making ◽  
Integrated Decision Making

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects.

Objective Evaluation of ESP System Based on Hardware-in-Loop Test Bench

2013 ◽  
Vol 694-697 ◽  
pp. 2080-2084
Author(s):  
Sheng Yi Xuan ◽  
Chuan Xue Song ◽  
Guang Wei Meng
Keyword(s):  
Control Algorithm ◽  
Evaluation Method ◽  
Test Bench ◽  
Objective Evaluation ◽  
Control Effect ◽  
Simulation Test ◽  
Pd Control ◽  
Time Test ◽  
Hardware In Loop ◽  
Hardware In Loop Simulation

The evaluation tests play an important role in modern vehicle development. As a mean of judging vehicles performance and quality in the design period, the evaluation tests guide the development and the design. In this paper, objective evaluation of ESP is researched based on the self-correcting threshold PD control algorithm and ESP hardware-in-loop simulation test bench. Real-time test bench is used to simulate the different conditions and the evaluation methods of ESP are tested to verify the effect. By hardware-in-loop simulation, the results demonstrate the objective evaluation method evaluates the ESP system in accordance with the results of self-correcting threshold PD control algorithm and the method could accurately reflect the ESP control effect.

scholarly journals Deep, Consistent Behavioral Decision Making with Planning Features for Autonomous Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1492 ◽  
Author(s):  
Lilin Qian ◽  
Xin Xu ◽  
Yujun Zeng ◽  
Junwen Huang
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Optimal Policy ◽  
Autonomous Vehicles ◽  
Partial Information ◽  
Strong Dependence ◽  
Autonomous Driving ◽  
Gradient Algorithm ◽  
Main Trend ◽  
Behavioral Decision

Autonomous driving promises to be the main trend in the future intelligent transportation systems due to its potentiality for energy saving, and traffic and safety improvements. However, traditional autonomous vehicles’ behavioral decisions face consistency issues between behavioral decision and trajectory planning and shows a strong dependence on the human experience. In this paper, we present a planning-feature-based deep behavior decision method (PFBD) for autonomous driving in complex, dynamic traffic. We used a deep reinforcement learning (DRL) learning framework with the twin delayed deep deterministic policy gradient algorithm (TD3) to exploit the optimal policy. We took into account the features of topological routes in the decision making of autonomous vehicles, through which consistency between decision making and path planning layers can be guaranteed. Specifically, the features of a route extracted from path planning space are shared as the input states for the behavioral decision. The actor-network learns a near-optimal policy from the feasible and safe candidate emulated routes. Simulation tests on three typical scenarios have been performed to demonstrate the performance of the learning policy, including the comparison with a traditional rule-based expert algorithm and the comparison with the policy considering partial information of a contour. The results show that the proposed approach can achieve better decisions. Real-time test on an HQ3 (HongQi the third ) autonomous vehicle also validated the effectiveness of PFBD.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

2020 ◽  
Vol 235 (1) ◽  
pp. 55-77
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Hojun Lee ◽  
Jongcherl Park ◽  
Kyongsu Yi
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Change ◽  
Driving Mode ◽  
Lane Changes ◽  
Safety Indices

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

Design and Implement on Intelligent Target Ship for Ship Handling Simulator

2011 ◽  
Vol 97-98 ◽  
pp. 854-858 ◽  
Author(s):  
Xing Hua Wang ◽  
Shen Hua Yang ◽  
Guo Quan Chen
Keyword(s):  
Decision Making ◽  
Automatic Control ◽  
Collision Avoidance ◽  
Control Algorithm ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Ship Handling

Aiming at the fact that target ships of ship handling simulator have not the ability of avoiding other ships according to the COLREG 1972, the paper puts forward to add Personifying Intelligent Decision-making for Vessel Collision Avoidance, for short PIDVCA, and vessel automatic control algorithm into solver procedure of target ship, designed and realized target ships to sail according to the COLREG 1972, which make ship handling simulator more realistic simulate the actual navigation environment at sea. Finally, the paper gives the simulation pictures of 2 and 4 target ships intelligent collision.

scholarly journals Research on Comparison of LiDAR and Camera in Autonomous Driving

2021 ◽  
Vol 2093 (1) ◽  
pp. 012032
Author(s):  
Peide Wang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Environmental Perception ◽  
Information Collection ◽  
Digital Signals ◽  
Perception System ◽  
Key Technologies ◽  
And Control ◽  
Research Hotspots

Abstract With the improvement of vehicles automation, autonomous vehicles become one of the research hotspots. Key technologies of autonomous vehicles mainly include perception, decision-making, and control. Among them, the environmental perception system, which can convert the physical world’s information collection into digital signals, is the basis of the hardware architecture of autonomous vehicles. At present, there are two major schools in the field of environmental perception: camera which is dominated by computer vision and LiDAR. This paper analyzes and compares the two majors schools in the field of environmental perception and concludes that multi-sensor fusion is the solution for future autonomous driving.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

scholarly journals Research prospect of autonomous driving decision technology under complex traffic scenarios

2022 ◽  
Vol 355 ◽  
pp. 03031
Author(s):  
Yaoguang Cao ◽  
Yuyi Chen ◽  
Lu Liu
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Knowledge Map ◽  
Autonomous Decision ◽  
Decision Making System ◽  
Research Prospect ◽  
Safety And Stability

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

Multi-Cameras based Decision Making at Mini-Roundabouts for Autonomous Vehicles

2019 ◽  
Author(s):  
Weichao Wang ◽  
Quang A Nguyen ◽  
Paul Wai Hing Chung ◽  
Qinggang Meng
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles
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