Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Sadid, Hashmatullah; Antoniou, Constantinos

doi:10.1049/itr2.12337

Cited by 11 publications

(6 citation statements)

References 104 publications

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“…The selection of value thresholds aligns with the findings presented in [43]. To ensure a valid safety estimation, the time-to-collision (TTC) and post-encroachment time (PET) thresholds were adopted as evaluation parameters, as they are widely used in the freeway context and prevalent in the safety analysis literature [44].…”

Section: Vissim Modelingmentioning

confidence: 96%

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

Granà,

Curto,

Petralia

et al. 2024

Vehicles

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The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations.

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Section: Vissim Modelingmentioning

confidence: 96%

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

Granà,

Curto,

Petralia

et al. 2024

Vehicles

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“…Updating the actor network parameter θ according to the deterministic policy gradient 1 N ∑ j ∇ a Q(s j , a; w 1 )| a=µ(s j ;θ) ∇ θ µ(s j ; θ) end for 22: end for…”

Section: Algorithm 1 Training Process Of Motion-prediction-based Td3 ...mentioning

confidence: 99%

“…With the continuous advancements in advanced driver assistance systems (ADASs) such as adaptive cruise control, cooperative adaptive driving control, lane-keeping assistance, and emergency brake assistance, the advent of a transformative era in autonomous transportation is imminent [1]. As an essential function of ADASs, adaptive cruise control, which is closely related to the CF strategy, is instrumental in improving driving comfort, lessening driver strain, enhancing precision in vehicle handling, and bolstering vehicular safety.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Car-Following Control Based on Multivehicle Motion Prediction

Wang,

Qu,

Wang

et al. 2024

Electronics

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Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of vehicle-to-vehicle (V2V) communication technologies, intelligent vehicles such as connected autonomous vehicles (CAVs) can gather information about surrounding vehicles. Therefore, this study proposes an RL-based CF control strategy that takes multivehicle scenarios into account. First, the trajectories of two preceding vehicles and one following vehicle relative to the subject vehicle (SV) are extracted from a highD dataset to construct the environment. Then the twin-delayed deep deterministic policy gradient (TD3) algorithm is implemented as the control strategy for the agent. Furthermore, a sequence-to-sequence (seq2seq) module is developed to predict the uncertain motion statuses of surrounding vehicles. Once integrated into the RL framework, this module enables the agent to account for dynamic changes in the traffic environment, enhancing its robustness. Finally, the performance of the CF control strategy is validated both in the highD dataset and in two traffic perturbation scenarios. In the highD dataset, the TD3-based prediction CF control strategy outperforms standard RL algorithms in terms of convergence speed and rewards. Its performance also surpasses that of human drivers in safety, efficiency, comfort, and fuel consumption. In traffic perturbation scenarios, the performance of the proposed CF control strategy is compared with the model predictive controller (MPC). The results show that the TD3-based prediction CF control strategy effectively mitigates undesired traffic waves caused by the perturbations from the head vehicle. Simultaneously, it maintains the desired traffic state and consistently ensures a stable and efficient traffic flow.

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“…This is possible for AVs, since they have the ability to detect the surrounding environment using their advanced sensing technologies (e.g., Lidar, Radar). The more advanced version of AVs, so-called connected AVs (CAVs), are capable of exchanging driving information (i.e., speed, acceleration, position, and more) not only with nearby connected vehicles (V2V) but also with connected vehicles in their communication range, 1 Hashmatullah Sadid is corresponding author, Chair of Transportation Systems Engineering, Technical University of Munich (TUM), hashmat.sadid@tum.de Manuscript received May 23, 2024 as well as infrastructure (V2I) [9]. Trajectory prediction is not important only for the motion prediction of AVs, but also for capturing their driving behaviors in terms of car-following and lane-changing.…”

Section: Introductionmentioning

confidence: 99%

“…Trajectory prediction is not important only for the motion prediction of AVs, but also for capturing their driving behaviors in terms of car-following and lane-changing. An accurate trajectory prediction model could be potentially integrated with a simulation tool to replicate the driving behavior of AVs and conduct a simulation-based impact assessment of AVs deployment scenarios in a traffic network [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spatio-temporal Graph Neural Network for Surrounding-aware Trajectory Prediction of Autonomous Vehicles

Sadid,

Antoniou

2024

IEEE Trans. Intell. Veh.

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Trajectory prediction is a critical aspect of understanding and estimating the motion of dynamic systems, including robotics and autonomous vehicles (AVs). For safe and efficient driving behavior, an AV should predict its own motion and the motions of surrounding vehicles in the upcoming time steps. To achieve this, understanding the interaction among vehicles is crucial for accurate trajectory prediction. In this research, we implement a dynamic Spatio-temporal graph convolutional network to predict the trajectory distribution of vehicles in a traffic scene. We perform the graph convolutional network (GCN) operation on directed graphs to capture the spatial dependencies among vehicles in each traffic scene. To accurately replicate the interaction among vehicles, we propose a novel weighted adjacency matrix derived by the strategic positions of vehicles (angular encoding) and the reciprocal of distances among vehicles in a traffic scene. Additionally, we employ the temporal convolution network (TCN) to learn the temporal dependencies of a trajectory sequence and decode the future driving status using historic trajectories. We test the model with a naturalistic trajectory dataset (HighD) and conduct performance evaluation. The findings reveal that the proposed model could significantly improve accuracy compared to existing state-ofthe-art models. Meanwhile, we conduct transfer learning to test the generalizability of our model on low data availability scenario using NGSIM (US-101) dataset. The results show that the relearned model perform comparability well and depicts competing performance in comparison to the state-of-the-art methods.

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Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Cited by 11 publications

References 104 publications

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

Deep Reinforcement Learning Car-Following Control Based on Multivehicle Motion Prediction

Dynamic Spatio-temporal Graph Neural Network for Surrounding-aware Trajectory Prediction of Autonomous Vehicles

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