Limitations and Improvements of the Intelligent Driver Model (IDM)

Albeaik, Saleh; Bayen, Alexandre M.; Chiri, Maria Teresa; Gong, Xiaoqian; Hayat, Amaury; Kardous, Nicolas; Keimer, Alexander; McQuade, Sean T.; Piccoli, Benedetto; You, Yiling

doi:10.1137/21m1406477

Cited by 23 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulations were conducted for traffic scenarios without autonomous vehicles at signalized crossroads, recording the trajectories of all surrounding vehicles controlled by the Intelligent Driver Model (IDM) [ 36 ]. By learning feature representations from this dataset, autonomous vehicles can infer features from other drivers’ trajectories before deciding to merge or wait.…”

Section: The Proposed Methodsmentioning

confidence: 99%

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Liu,

Diao

2024

PLoS ONE

View full text Add to dashboard Cite

As autonomous driving technology continues to advance and gradually become a reality, ensuring the safety of autonomous driving in complex traffic scenarios has become a key focus and challenge in current research. Model-free deep reinforcement learning (Deep Reinforcement Learning) methods have been widely used for addressing motion planning problems in complex traffic scenarios, as they can implicitly learn interactions between vehicles. However, current planning methods based on deep reinforcement learning exhibit limited robustness and generalization performance. They struggle to adapt to traffic conditions beyond the training scenarios and face difficulties in handling uncertainties arising from unexpected situations. Therefore, this paper addresses the challenges presented by complex traffic scenarios, such as signal-free intersections. It does so by first utilizing the historical trajectories of adjacent vehicles observed in these scenarios. Through a Variational Auto-Encoder (VAE) based on the Gated Recurrent Unit (GRU) recurrent neural network, it extracts driver style features. These driver style features are then integrated with other state parameters and used to train a motion planning strategy within an extended reinforcement learning framework. This approach ultimately yields a more robust and interpretable mid-to-mid motion planning method. Experimental results confirm that the proposed method achieves low collision rates, high efficiency, and successful task completion in complex traffic scenarios.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Liu,

Diao

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The IDM is a model designed to maintain a safe following distance based on the cognitive reaction time and acceleration of individual vehicles. While IDMs do a good job of representing differences in perceptual reaction time, they have the limitation that the speed of a particular vehicle may be negative at certain times [18].…”

Section: Related Workmentioning

confidence: 99%

Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration

Shin,

Cho,

Lee

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study analyzes the impact of autonomous-vehicle (AV) market-penetration rates on traffic-flow safety using a genetic algorithm. We set up a microscopic traffic-simulation scenario on a 640 m section of the US I-101 freeway using VISSIM, a microscopic traffic-simulation software. The results of analyzing the number of conflicts according to the introduction rate of autonomous vehicles showed that the number of conflicts increased as the introduction rate increased up to 30%, and then decreased from 40% or more. In this study, it was assumed that autonomous vehicles can avoid dangerous situations, so it is judged that this is the result of an increase in the traffic volume of autonomous vehicles and a decrease in the traffic volume of conventional vehicles. When planning an exclusive lane for autonomous vehicles, it is judged that it is desirable to install two exclusive lanes on the left side until the introduction rate of autonomous vehicles reaches 30%. When the introduction rate of autonomous vehicles is 40–90%, the risk of accidents between autonomous vehicles and conventional vehicles decreases, and the traffic volume of autonomous vehicles is higher than that of conventional vehicles. Therefore, it is judged that it is desirable to operate a mixed road where autonomous vehicles and conventional vehicles can drive together rather than operating an exclusive lane for autonomous vehicles.

show abstract

“…In the second scenario, assuming that the vehicle in front is driving at a speed of v front , if the distance to the vehicle in front meets the speed and acceleration requirements, the host vehicle will still pass or stop according to the remaining green phase of the upcoming traffic light. While the vehicle's speed tolerance is affected by the vehicle in front, the host vehicle must obey intelligent driving model (IDM) rules [36]. The speed limit is v max = max {v front , (1 + ε)v d , v lim }.…”

Section: Eco-driving Objective Functionmentioning

confidence: 99%

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Xu,

Zhang,

et al. 2023

Sustainability

View full text Add to dashboard Cite

Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions.

show abstract

Limitations and Improvements of the Intelligent Driver Model (IDM)

Cited by 23 publications

References 22 publications

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Contact Info

Product

Resources

About