Evaluating human &amp;amp; computer for expressway lane changing

Tehrani, Hossein; Muto, Kenji; Yoneda, Keisuke; Mita, Seiichi

doi:10.1109/ivs.2014.6856522

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…Lefèvre et al [30] present a framework for autonomous driving which can learn from human demonstrations, and apply the demonstrations to the longitudinal control of an autonomous car. Tehrani et al [32] compare the actions of a human driver with computer generated motions for expressway lane changing. By analyzing the human-driver lane change data, Do et al [33] propose a two-segment lane change model that mimics the human driver.…”

Section: Introductionmentioning

confidence: 99%

Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

et al. 2019

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With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks. INDEX TERMS Autonomous vehicle, distraction monitoring, velocity control, model predictive control, convolutional neural networks.

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Section: Introductionmentioning

confidence: 99%

Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

et al. 2019

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“…They propose to provide "ease and comfort" by producing jerk-optimal trajectories. Later, lane change experiments conducted by Tehrani et al, in Japanese highways showed that the human lane change behavior cannot be modelled by a single stage of jerk-optimal trajectories [5,6]. Instead they proposed a two-stage model.…”

Section: Introductionmentioning

confidence: 99%

Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

Sharifzadeh,

Chiotellis,

Triebel

et al. 2016

Preprint

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We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.

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An Improved Grey Relational Theory Evaluation Method: Considering the Comprehensive Performance of Autonomous Vehicles in Virtual Test

Wang,

Li,

et al. 2024

Int.J Automot. Technol.

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Evaluating human & computer for expressway lane changing

Cited by 10 publications

References 13 publications

Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

An Improved Grey Relational Theory Evaluation Method: Considering the Comprehensive Performance of Autonomous Vehicles in Virtual Test

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