Decision - making of Lane Change Behavior Based on RCS for Automated Vehicles in the Real Environment

Xiong, Guangming; Kang, Ziyi; Li, Hao; Song, Wei; Jin, Yaying; Gong, Jianwei

doi:10.1109/ivs.2018.8500651

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…While reducing the OAAR, it also sacrifices ATC to Multi-lane merging from side-ways (Kernel width:σ = 50) Algorithm Samples Training time OAAR 1 ATC 2 KLSPI 30000 1557s 5.4% 3.93s USP-KLSPI 30000 1401s 4.90% 4.23s 1 OAAR: Obstacle avoidance assistance rate in 1000 tests. 2 ATC: The average time cost in 1000 tests. If a merging task is not completed in 10s, the time cost is still counted as 10s.…”

Section: B Multi-lane Merging From Side-waysmentioning

confidence: 99%

“…In a typical decision-making and motion-planning system, the decision-making module is in charge of generating highlevel orders, such as slow down and speed up; while the mo-tion planner is to compute the detailed trajectory profile that can be followed by the vehicle using low-level controllers. There have been fruitful works contributed to improving the decision-making behaviors in complex environment and the motion planning performance in terms of mobility and smoothness, see for instance [2], [3]. The above works addressed the decision-making or motion planning problems respectively.…”

Section: Introductionmentioning

confidence: 99%

“…ATC: the average time cost in 1000 tests. If a left-turn task is not completed in 10s, the time cost is still counted as 10s 2. The completion rate: If the task is completed within 10 seconds then it is considered a success; Otherwise, it is considered a failure.…”

mentioning

confidence: 99%

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Hierarchical Reinforcement Learning for Autonomous Decision Making and Motion Planning of Intelligent Vehicles

Yang

Zhang

et al. 2020

IEEE Access

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Autonomous decision making and motion planning in complex dynamic traffic environments, such as left-turn without traffic signals and multi-lane merging from sideways , are still challenging tasks for intelligent vehicles. It is difficult to generate optimized behavior decisions while considering the motion capabilities and dynamic properties of intelligent vehicles. Aiming at the above problems, this paper proposes a hierarchical reinforcement learning approach for autonomous decision making and motion planning in complex dynamic traffic scenarios. The proposed approach consists of two layers. At the higher layer, a kernel-based least-squares policy iteration algorithm with uneven sampling and pooling strategy (USP-KLSPI) is presented for solving the decision-making problems. The motion capabilities of the ego vehicle and the surrounding vehicles are evaluated with a high-fidelity dynamic model in the decisionmaking layer. By doing so, the consistency between the decisions generated at the higher layer and the operations in the lower planning layer can be well guaranteed. The lower layer addresses the motionplanning problem in the lateral direction using a dual heuristic programming (DHP) algorithm learned in a batch-mode manner, while the velocity profile in the longitudinal direction is inherited from the higher layer. Extensive simulations are conducted in complex traffic conditions including left-turn without traffic signals and multi-lane merging from sideways scenarios. The results demonstrate the effectiveness and efficiency of the proposed approach in realizing optimized decision making and motion planning in complex environments. INDEX TERMS Autonomous driving, hierarchical reinforcement learning, complex dynamic traffics, decision making, motion planning.

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Section: B Multi-lane Merging From Side-waysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Reinforcement Learning for Autonomous Decision Making and Motion Planning of Intelligent Vehicles

Yang

Zhang

et al. 2020

IEEE Access

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“…The FSM method is a representative model in the rule-based category, which has an interpretable and simple structure based on if-then-else logic, and on the small size of the tuning parameters [8]. A hierarchy state machine was proposed in [9], which addresses the lane-changing decisions for AVs, and a heuristic-based FSM was proposed in [10] for evaluating safety regions and generating reasonable driving behavior. For learning-based methods, deep learning, reinforcement learning and inverse reinforcement learning are usually applied.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Wang

Song

et al. 2023

Sustainability

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Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities.

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“…To accurately simulate the driver's lane-changing behaviour, machine learning (ML) methods (such as SVM, DBN, HMM) are used to make lane-changing behaviour decisions for autonomous vehicles in [14][15][16][17][18][19][20]. Most ML-based models divide the collected data samples into sections and describe the driver's lane-change behaviour by extracting the characteristics of these parts of the data.…”

Section: Introductionmentioning

confidence: 99%

Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles

Jiang

Duan

Liu³

et al. 2020

IET Intelligent Trans Sys

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To solve the unacceptable issue caused by the inconsistency of lane-changing behaviour between autonomous vehicles and actual drivers. A lane-changing behaviour decision-making model based on the Gauss mixture hidden Markov model (GM-HMM) is proposed according to the characteristic of a driver's lane changing behaviour. The proposed model is tested and verified based on the database of Next-Generation Simulation (NGSIM). The results show that the GM-HMM is 95.4% similar to the real driver's behaviour. To further verify the proposed model, the proposed algorithm is compared with some machine learning techniques from literature in different test scenarios. The comparison and analysis indicate that the GM-HMM method can more accurately simulate the real driver's lane-change behaviour, thus improving the trust of the passengers and other vehicles around autonomous vehicles.

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Decision - making of Lane Change Behavior Based on RCS for Automated Vehicles in the Real Environment

Cited by 16 publications

References 7 publications

Hierarchical Reinforcement Learning for Autonomous Decision Making and Motion Planning of Intelligent Vehicles

Hierarchical Reinforcement Learning for Autonomous Decision Making and Motion Planning of Intelligent Vehicles

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles

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