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.

MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties

2019 ◽  
Author(s):  
Sai Rajeev Devaragudi ◽  
Bo Chen
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Constant Time ◽  
Longitudinal Motion ◽  
Driver Assistance Systems

Abstract This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.

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 Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

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.

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.

Path planning of AUV during diving process based on behavioral decision-making

2021 ◽  
Vol 234 ◽  
pp. 109073
Author(s):  
Guanzhong Chen ◽  
Yue Shen ◽  
Nanzhu Qu ◽  
Bo He
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Behavioral Decision Making ◽  
Behavioral Decision

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.

Sign in / Sign up

Export Citation Format

Share Document