Strategy for automated dense parking: how to navigate in narrow lanes

Polack, Philip; Dallen, Louis-Marie; Cord, A.

doi:10.1109/icra40945.2020.9197088

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Michalke et al., [16] developed a machine learning based road navigation system, specifically for driving through narrow roads. Similarly, Polack et al., [17] developed a control strategy to drive CAVs in a narrow space to guarantee the movement of vehicles in a confined space. Other studies have tried comparing the effects on human driver behaviour of driving in narrow and regular lanes [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing temporary traffic management measures on a motorway: Lane closures vs narrow lanes for connected and autonomous vehicles in roadworks

Singh,

Formosa,

Man

et al. 2024

IET Intelligent Trans Sys

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Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM‐based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle‐kilometre‐travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing temporary traffic management measures on a motorway: Lane closures vs narrow lanes for connected and autonomous vehicles in roadworks

Singh,

Formosa,

Man

et al. 2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

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“…A collision-free path sometimes is unachievable under some circumstances when using obstacle dilation on a map. Research [24] used a Lyapunov-based control strategy to move a car-like robot in a narrow lane. However, this method costs daunting computations and requires a parameterized environment that is not available in exploration scenarios.…”

Section: Narrow Space Motion Problemmentioning

confidence: 99%

Reinforcement Learning for Self-exploration in Narrow Spaces

Tian¹,

Liu²,

Zhou³

et al. 2022

Preprint

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In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises. Additionally, it is disabled when mapless. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a map while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the destination information. Then we benchmark five reinforcement learning algorithms including DDPG, DQN, SAC, PPO, and PPO-discrete, in a simulated narrow track. After training, the well-performed DDPG and DQN models can be transferred to three brand new simulated tracks, and furthermore to three real-world tracks. https://sites.google.com/view/rl4exploration

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A Multi-Objective Parking Space Decision-Making and Path Planning Method

Wang

Yue

et al. 2021

2021 IEEE International Conference on Unmanned Systems (ICUS)

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Strategy for automated dense parking: how to navigate in narrow lanes

Cited by 5 publications

References 19 publications

Assessing temporary traffic management measures on a motorway: Lane closures vs narrow lanes for connected and autonomous vehicles in roadworks

Assessing temporary traffic management measures on a motorway: Lane closures vs narrow lanes for connected and autonomous vehicles in roadworks

Reinforcement Learning for Self-exploration in Narrow Spaces

A Multi-Objective Parking Space Decision-Making and Path Planning Method

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