Re-Plannable Automated Parking System With a Standalone Around View Monitor for Narrow Parking Lots

Jang, Chang‐Hyun; Kim, Chan-Soo; Lee, Seongjin; Kim, Seokwon; Lee, Sumyeong; Sunwoo, Myoungho

doi:10.1109/tits.2019.2891665

Cited by 42 publications

(35 citation statements)

References 48 publications

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“…The real vehicle experimental results showed that the final parking performance of DERL, which did not use system identification, was better than that of MCTS guided by a policy network using a refined vehicle model, which planed fewer motions and was more affected by vehicle model errors and perception errors. Similar to [ 1 , 8 , 19 ], re-planning proved to be a powerful tool for autonomous driving systems under perception uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…The AVM system can continuously track the parking slot. To obtain the slot marking result, deep convolutional network-based segmentation methods were proposed [ 1 , 4 ]. For the tracking of the object, the prediction accuracy is deteriorated by noises, and the occlusion should be considered.…”

Section: Related Workmentioning

confidence: 99%

“…The former includes numerical optimizations, A* search, rapidly exploring random tree (RRT) family, and curve planners. In the optimization methods, local optimization [ 1 , 19 ] was developed to find the short-range path. Although the global optimality cannot be guaranteed, they usually generated feasible paths in a short time.…”

Section: Related Workmentioning

confidence: 99%

“…Automated parking systems (APSs) are important due to their great potential to reduce accidents in narrow urban parking spaces and increase parking space use [ 1 , 2 ]. For all APS platforms, the intelligent vehicle must generate its motion after the parking space is detected by the on-board sensors system, such as Around View Monitoring (AVM) [ 3 , 4 ] and Light Detection and Ranging (LiDAR) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Song

Chen

Sun

et al. 2020

Sensors

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Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many interactions to achieve acceptable performance, and model-based RL in APS cannot continuously learn. In this paper, a data-efficient RL method is constructed to learn from data by use of a model-based method. The proposed method uses a truncated Monte Carlo tree search to evaluate parking states and select moves. Two artificial neural networks are trained to provide the search probability of each tree branch and the final reward for each state using self-trained data. The data efficiency is enhanced by weighting exploration with parking trajectory returns, an adaptive exploration scheme, and experience augmentation with imaginary rollouts. Without human demonstrations, a novel training pipeline is also used to train the initial action guidance network and the state value network. Compared with path planning and path-following methods, the proposed integrated method can flexibly co-ordinate the longitudinal and lateral motion to park a smaller parking space in one maneuver. Its adaptability to changes in the vehicle model is verified by joint Carsim and MATLAB simulation, demonstrating that the algorithm converges within a few iterations. Finally, experiments using a real vehicle platform are used to further verify the effectiveness of the proposed method. Compared with obtaining rewards using simulation, the proposed method achieves a better final parking attitude and success rate.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Song

Chen

Sun

et al. 2020

Sensors

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“…Again, autonomous driving has attracted remarkable attention in recent years in diverse traffic scenarios, including lane changing [12,13], reverse parking [14], parallel parking [15], longitudinal control [16], computing resource allocation [17] and motion/trajectory design [18][19][20][21][22]. Among all these challenges, the planning of the AVs' trajectory is fundamental.…”

Section: A State-of-the-artmentioning

confidence: 99%

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. The problem of driving trajectory design is formulated for minimizing the total fuel consumption, while enhancing driving safety (by obeying the traffic rules and avoiding obstacles). In an effort to solve this pertinent problem, a deep reinforcement learning (DRL) approach is proposed for making collision-free decisions. Firstly, a deep Q-network (DQN) aided algorithm is proposed for determining the trajectory and velocity of the AV by receiving real-time traffic information from the base stations (BSs). More particularly, the AV acts as an agent to carry out optimal action such as lane change and velocity change by interacting with the environment. Secondly, to overcome the large overestimation of action values by the Q-learning model, a double deep Q-network (DDQN) algorithm is proposed by decomposing the max-Q-value operation into action selection and action evaluation. Additionally, three practical driving policies are also proposed as benchmarks. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

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Multi-scenario Automatic Parking Based on Deep Reinforcement Learning

Yang,

Tang,

Cai

2024

Lecture Notes in Civil Engineering

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Re-Plannable Automated Parking System With a Standalone Around View Monitor for Narrow Parking Lots

Cited by 42 publications

References 48 publications

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Multi-scenario Automatic Parking Based on Deep Reinforcement Learning

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