A Review of Vision-Based Traffic Semantic Understanding in ITSs

Chen, Jing; Wang, Qichao; Cheng, Harry H.; Peng, Weiming; Xu, Wenqiang

doi:10.1109/tits.2022.3182410

Cited by 112 publications

(31 citation statements)

References 230 publications

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“…The severity of traffic accidents is a significant indicator of traffic accident injury. There are a variety of elements that contribute to traffic accidents of varying severity [ 6 , 7 ]. In the last 20 years, no substantial reduction in traffic accident fatalities and injuries has been observed.…”

Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

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Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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“…To reach this goal, the implementation of smart mobility requires not only a robustly connected infrastructure connecting vehicles and their environment but also appropriated algorithms and methods for effective management and coordination of vehicle movements. For example, semantic traffic understanding might be exploited to define the number of vehicles in a lane by means of vision-based algorithms [ 3 ]. Indeed, road traffic is an extremely complex system and intersections, in particular, pose a significant challenge: a delicate balance is required between assigning priority in a fair way and the flexibility needed for emergency situations.…”

Section: Introductionmentioning

confidence: 99%

Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections

Cabri,

Lugli,

Montangero

et al. 2024

Sensors

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With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario.

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“…Deep learning (DL) has emerged as a powerful technique for extracting discriminative and salient features in high-level action and behavior recognition from video data ( Dai et al, 2019 ; Zhang et al, 2021 ; Chen et al, 2022 ). Present DL methods employed in human action recognition (HAR) are constructed based on basic CNNs for extracting features from video frames through the utilization of pre-trained models.…”

Section: Introductionmentioning

confidence: 99%

Attention-enhanced gated recurrent unit for action recognition in tennis

Gao,

2024

PeerJ Computer Science

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Human Action Recognition (HAR) is an essential topic in computer vision and artificial intelligence, focused on the automatic identification and categorization of human actions or activities from video sequences or sensor data. The goal of HAR is to teach machines to comprehend and interpret human movements, gestures, and behaviors, allowing for a wide range of applications in areas such as surveillance, healthcare, sports analysis, and human-computer interaction. HAR systems utilize a variety of techniques, including deep learning, motion analysis, and feature extraction, to capture and analyze the spatiotemporal characteristics of human actions. These systems have the capacity to distinguish between various actions, whether they are simple actions like walking and waving or more complex activities such as playing a musical instrument or performing sports maneuvers. HAR continues to be an active area of research and development, with the potential to enhance numerous real-world applications by providing machines with the ability to understand and respond to human actions effectively. In our study, we developed a HAR system to recognize actions in tennis using an attention-based gated recurrent unit (GRU), a prevalent recurrent neural network. The combination of GRU architecture and attention mechanism showed a significant improvement in prediction power compared to two other deep learning models. Our models were trained on the THETIS dataset, one of the standard medium-sized datasets for fine-grained tennis actions. The effectiveness of the proposed model was confirmed by three different types of image encoders: InceptionV3, DenseNet, and EfficientNetB5. The models developed with InceptionV3, DenseNet, and EfficientNetB5 achieved average ROC-AUC values of 0.97, 0.98, and 0.81, respectively. While, the models obtained average PR-AUC values of 0.84, 0.87, and 0.49 for InceptionV3, DenseNet, and EfficientNetB5 features, respectively. The experimental results confirmed the applicability of our proposed method in recognizing action in tennis and may be applied to other HAR problems.

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A Review of Vision-Based Traffic Semantic Understanding in ITSs

Cited by 112 publications

References 230 publications

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections

Attention-enhanced gated recurrent unit for action recognition in tennis

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