Artificial Intellgence Based Decision Making of Autonomous Vehicles Before Entering Roundabout

被引:0
|
作者
Tollner, David [1 ]
Cao, Hang [2 ]
Zoldy, Mate [2 ]
机构
[1] Tech Univ Budapest, Dept Differential Equat, Budapest, Hungary
[2] Tech Univ Budapest, Dept Automot Technol, Budapest, Hungary
来源
IEEE JOINT 19TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS AND 7TH INTERNATIONAL CONFERENCE ON RECENT ACHIEVEMENTS IN MECHATRONICS, AUTOMATION, COMPUTER SCIENCES AND ROBOTICS (CINTI-MACRO 2019) | 2019年
关键词
autonomous vehicle; roundabout; neural network;
D O I
10.1109/cinti-macro49179.2019.9105322
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Decisions of entering into a roundabout or stop before are multi-criterial situations that are complex for both human drivers and for the self-driving vehicles as well. Complexity of the decision is the reason to involve artificial intelligence. Creating standards for autonomous vehicle artificial intelligence based decisions at roundabout entering situation needs deeper understanding of vehicle and traffic behaviour, parametrizing, modelling and simulation. In our research paper, we present an overview about the literature of autonomous vehicles roundabout related decisions. Based on the overviewed cases main parameters of the decision situation was determined. A commonly utilized modeling environment was used for the first simulations and data gathering for feeding the developed neural networks. In this paper, we give an overview about the status of the research, the developed roundabout traffic, the complexity of the decision and the first test results about autonomous vehicle decision before entering into the roundabout supported with artificial intelligence.
引用
收藏
页码:181 / 186
页数:6
相关论文
共 50 条
  • [21] A Data-Driven Optimal Control Decision-Making System for Multiple Autonomous Vehicles
    Kang, Liuwang
    Shen, Haiying
    2021 ACM/IEEE 6TH SYMPOSIUM ON EDGE COMPUTING (SEC 2021), 2021, : 192 - 201
  • [22] Robust Lane Change Decision Making for Autonomous Vehicles: An Observation Adversarial Reinforcement Learning Approach
    He, Xiangkun
    Yang, Haohan
    Hu, Zhongxu
    Lv, Chen
    IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (01): : 184 - 193
  • [23] TAIL-DRL:Adaptive Lane Change Decision Making Of Autonomous Vehicles In Mixed Traffic
    Yu, Junru
    Xu, Risheng
    Wang, Xiao
    Mu, Chaoxu
    2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024, 2024, : 381 - 386
  • [24] Risk-informed decision-making and control strategies for autonomous vehicles in emergency situations
    Nguyen, Hung Duy
    Choi, Mooryong
    Han, Kyoungseok
    ACCIDENT ANALYSIS AND PREVENTION, 2023, 193
  • [25] Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends
    Liu, Qi
    Li, Xueyuan
    Tang, Yujie
    Gao, Xin
    Yang, Fan
    Li, Zirui
    SENSORS, 2023, 23 (19)
  • [26] Artificial intelligence based object detection and traffic prediction by autonomous vehicles - A review
    Preeti
    Rana, Chhavi
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
  • [27] Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees
    He, Xiangkun
    Huang, Wenhui
    Lv, Chen
    ENGINEERING, 2024, 33 : 77 - 89
  • [28] Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
    Li, Guofa
    Yang, Yifan
    Li, Shen
    Qu, Xingda
    Lyu, Nengchao
    Li, Shengbo Eben
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2022, 134
  • [29] Toward personalized decision making for autonomous vehicles: A constrained multi-objective reinforcement learning technique
    He, Xiangkun
    Lv, Chen
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 156
  • [30] Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization
    Feng, Fuyong
    Wei, Chao
    Zhao, Botong
    Lv, Yanzhi
    He, Yuanhao
    SENSORS, 2024, 24 (05)