In mixed-traffic of human-driven vehicles (HDVs) and Connected and Automated Vehicles (CAVs), the trajectory planning of CAV is a critical issue. This study proposes a Hierarchical Dynamic Trajectory Planning Framework for CAVs. The framework consists of two layers. At the path planning layer, an ID* Lite algorithm is proposed with four enhancement modules: a risk-aware target update module, a spatiotemporal search module, a look-ahead safety inspection module, and a path smoothing module. At the trajectory tracking layer, a Model Predictive Control (MPC) algorithm refines the planned path by imposing vehicle dynamics and input constraints. Simulation experiments were conducted on a MATLAB-based upstream lane scenario of a signalized intersection. Results show that the proposed ID* Lite significantly outperforms state-of-the-art baselines. Specifically, the planning success rate, average path length, node expansion, and computation time are improved by 50%, 12.16%, 23.29%, and 20%, respectively. The hierarchical dynamic safe trajectory planning framework enhances motion control, reducing steering angle changes by 12.06%, endpoint error by 52.42%, and trajectory length by 0.07%. The results highlight the proposed framework’s effectiveness in achieving safe, real-time, smooth, and stable trajectory planning in mixed-traffic environments, suggesting its potential for autonomous driving applications.

The effective control of connected and automated vehicles (CAVs) in mixed platoons offers fresh opportunities to optimize the emerging mixed traffic flow environment in the future. The goals of existing studies are modeling accuracy and control effectiveness. As a continued work of such a pursuit, this article develops a data-driven robust predictive control framework for the mixed platoon composed of CAVs and human driven vehicles (HDVs). A deep variational Koopman network (DVKoN) was proposed to learn the HDVs’ uncertainty-aware dynamics driving behavior based on the HighD dataset. A DVKoN-based robust predictive control framework (DVKoRPC) was designed for optimizing the mixed vehicle platoon. The DVKoRPC has two components, i.e., a nominal system and an error system. The nominal system, which integrates multiple DVKoNs based on the platoon formation, is used as the state predictive model for the mixed vehicle platoon. The error system is used to compensate for deviations and uncertainties within platoon operation. Moreover, the asymptotic stability of the mixed vehicle platoon with the DVKoRPC was proved using Lyapunov theory. The experimental was conducted to verify the proposed DVKoN and DVKoRPC. The results show that DVKoN can accurately predict the uncertain car-following behavior of HDVs in the mixed vehicle platoon. The proposed DVKoRPC can effectively alleviate traffic oscillations, improve traffic efficiency, and reduce fuel consumption.

Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multi-scale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Moreover, to address the scarcity of traffic sign datasets, especially under adverse weather conditions, we introduce two novel datasets: Generated-TT100K-weather and CAWTSSS. Extensive evaluations conducted on challenging public benchmarks—including TT100K, CCTSDB2021, and GTSDB—as well as on our proposed datasets, demonstrate that YOLO-TS surpasses current state-of-the-art methods in both accuracy and inference speed. The code, datasets and weights are available athttps://github.com/Heqiang-Huang/YOLO-TS

This paper proposes a novel normalizing flow learning based method IIAG-CoFlow for low-light image enhancement (LLIE), which consists of an inter-and intra-channel attention Transformer based conditional generator (IIAG) and a complete flow (CoFlow). On the one hand, IIAG is designed as a U-shape network, whose down-sampling and up-sampling layers are constructed by IIZAT (i.e., inter-and intra-channel and zero-map attention Transformer) and IIAT (i.e., inter-and intra-channel attention Transformer) respectively. IIAT is designed to calculate inter-channel attention and intra-channel attention independently. Based on IIAT, IIZAT is designed to perform parallel fusion of zero-map attention and intra-channel attention. On the other hand, based on existing normalizing flow, we bring in unconditional affine coupling layer and design 3 invertible linear transformation layers, to develop CoFlow. The height and width axes based cross attention network (HWCAN) is proposed to learn affine/linear transformation parameters for conditional feature-driven layers of CoFlow. Experiments show that IIAG-CoFlow outperforms existing SOTA LLIE methods on several benchmark low-light datasets, and real NTM images. The source codes and pre-trained models are available athttps://github.com/NJUPT-IPR-ChenTS/IIAG-CoFlow.

We observe that the low-light RGB images, as well as night traffic monitoring (NTM) images, contain lots of color pixels with zeros caused by the low-light, which means that the low-light images suffer both information weakness and information loss of zero-element pixels. In this paper, we propose a novel flow-based generative method JTE-CFlow for low-light image enhancement, which consists of a joint-attention transformer based conditional encoder (JTE) and a map-wise cross affine coupling flow (CFlow). Specifically, JTE executes short-range and long-range operations by RRDBs (i.e., residual-in-residual dense blocks) and JATs (i.e., joint-attention transformer blocks) in series connection. JAT achieves weak information amplification and information loss restoration of zero-element pixels by the integration of self-attention and specific-attention with sharing the same value vectors, where the query and key vectors of specific-attention are from the zero-map feature of the low-light image. On the other hand, CFlow develops a map-wise cross affine coupling (MCAC) layer to perform cross learning for the flow feature, and a multiplication coupling network (MCN) to learn the transformation parameters of MCAC. JTE-CFlow learns to map the subtraction of outputs of CFlow and JTE (i.e., the residual code) into a standard normal distribution, and the inverse network of CFlow takes the latent feature of the low-light image as its input to infer the enhanced image. Experiments show that JTE-CFlow outperforms most SOTA methods on 7 mainstream low-light datasets with the same architecture, and can be applied to enhance NTM images. The source code and pre-trained models are available athttps://github.com/NJUPT-IPR-HuYin/JTE-CFlow.

This paper proposes a fast HSI (hue, saturation, intensity) color space and an orthogonal triangular with column pivoting (QRCP) enhancement model to tackle the large size RGB (red, green, blue) night traffic monitoring (NTM) image. Firstly, the fast HSI (FHSI) is proposed to decompose the light and color information of the RGB image, whose hue is defined as the cosine value of the included angle, instead of the included angle in HSI. The saturation of FHSI is defined as the ratio of the projection vector length and the side length of the projection equilateral triangle, and a saturation correction model is further proposed to correct color distortion of the low-light image by adjusting the saturation of FHSI. FHSI is more concise and faster than HSI. Secondly, a novel QRCP enhancement (QRCPE) model is proposed to improve the light of the low-light image by enhancing the intensity of FHSI, which first strengthens diagonal elements of QRCP, and followed by controlling the normalization of strengthened diagonal elements of QRCP. Finally, the FHSI-QRCPE based RGB image can be obtained by transforming the processed FHSI to RGB. The experimental results on NTM, SICE, ExDark, and BDD 100K databases, indicate that the proposed FHSI-QRCPE is fast and efficient to tackle low-light image enhancement.

This paper proposes HSV (hue, saturation, value) with three sectors (HSV-3S) and two-dimensional gradient descent algorithm (2D-GDA) for high-saturation low-light image enhancement in night traffic monitoring (NTM). The saturation of HSV-3S is defined as the ratio of the projection vector length and twice length of the sector start vector, which results in that the saturation of HSV-3S is smaller than that of HSV, and a saturation weakening model is proposed to further decrease the saturation of HSV-3S. The hue of HSV-3S is defined as the cosine value of the included angle between the projection vector and the sector start vector in each of three sectors. HSV-3S is more concise and faster than HSV. Then, 2D-GDA extends the gradient descent algorithm to 2D image domain. 2D-GDA employs the iteration matrix with variable step values (i.e., the step values of the dark regions are less than those of the bright regions), which can improve the pixel distribution of the 2D-GDA enhanced image. Finally, the HSV-3S+2D- GDA based RGB image can be obtained by performing 2D-GDA on the value of HSV-3S with transforming the processed HSV-3S to RGB. The experimental results on NTM (i.e., the brevity name of the database collected from real ITS), LOL, ExDark and SICE databases, indicate that HSV-3S+2D-GDA is fast and efficient for high-saturation low-light image enhancement.