Xianbiao Hu

Qing Tang, Xianbiao Hu

The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA), a leader-follower style vehicle system, utilizes connected and automated vehicle capabilities to enhance safety during transportation infrastructure maintenance in work zones. However, the speed difference between ATMA vehicles and general vehicles creates a moving bottleneck that reduces capacity and increases queue length, resulting in additional delays. The different routes taken by ATMA cause diverse patterns of time-varying capacity drops, which may affect the user equilibrium traffic assignment and lead to different system costs. This manuscript focuses on optimizing the routing for ATMA vehicles in a network to minimize the system cost associated with the slow-moving operation. To achieve this, a queuing-based traffic assignment approach is proposed to identify the system cost caused by the ATMA system. A queuing-based time-dependent (QBTD) travel time function, considering capacity drop, is introduced and applied in the static user equilibrium traffic assignment problem, with a result of adding dynamic characteristics. Subsequently, we formulate the queuing-based traffic assignment problem and solve it using a modified path-based algorithm. The methodology is validated using a small-size and a large-size network and compared with two benchmark models to analyze the benefit of capacity drop modeling and QBTD travel time function. Furthermore, the approach is applied to quantify the impact of different routes on the traffic system and identify an optimal route for ATMA vehicles performing maintenance work. Finally, sensitivity analysis is conducted to explore how the impact changes with variations in traffic demand and capacity reduction.

Ke Li, Tianjia Yang, Kaidi Liang et al.

Video prediction is a useful function for autonomous driving, enabling intelligent vehicles to reliably anticipate how driving scenes will evolve and thereby supporting reasoning and safer planning. However, existing models are constrained by multi-stage training pipelines and remain insufficient in modeling the diverse motion patterns in real driving scenes, leading to degraded temporal consistency and visual quality. To address these challenges, this paper introduces the historical motion priors-informed diffusion model (HMPDM), a video prediction model that leverages historical motion priors to enhance motion understanding and temporal coherence. The proposed deep learning system introduces three key designs: (i) a Temporal-aware Latent Conditioning (TaLC) module for implicit historical motion injection; (ii) a Motion-aware Pyramid Encoder (MaPE) for multi-scale motion representation; (iii) a Self-Conditioning (SC) strategy for stable iterative denoising. Extensive experiments on the Cityscapes and KITTI benchmarks demonstrate that HMPDM outperforms state-of-the-art video prediction methods with efficiency, achieving a 28.2% improvement in FVD on Cityscapes under the same monocular RGB input configuration setting. The implementation codes are publicly available at https://github.com/KELISBU/HMPDM.

Kaidi Liang, Ke Li, Xianbiao Hu et al.

Automating crash video analysis is essential to leverage the growing availability of driving video data for traffic safety research and accountability attribution in autonomous driving. Crash video analysis is a challenging multitask problem due to the complex spatiotemporal dynamics of crash events in video data and the diverse analytical requirements involved. It requires capabilities spanning crash recognition, temporal grounding, and high-level video understanding. Existing models, however, cannot perform all these tasks within a unified framework, and effective training strategies for such models remain underexplored. To fill these gaps, this paper proposes CrashChat, a multimodal large language model (MLLM) for multitask traffic crash analysis, built upon VideoLLaMA3. CrashChat acquires domain-specific knowledge through instruction fine-tuning and employs a novel multitask learning strategy based on task decoupling and grouping, which maximizes the benefit of joint learning within and across task groups while mitigating negative transfer. Numerical experiments on consolidated public datasets demonstrate that CrashChat consistently outperforms existing MLLMs across model scales and traditional vision-based methods, achieving state-of-the-art performance. It reaches near-perfect accuracy in crash recognition, a 176\% improvement in crash localization, and a 40\% improvement in the more challenging pre-crash localization. Compared to general MLLMs, it substantially enhances textual accuracy and content coverage in crash description and reasoning tasks, with 0.18-0.41 increases in BLEU scores and 0.18-0.42 increases in ROUGE scores. Beyond its strong performance, CrashChat is a convenient, end-to-end analytical tool ready for practical implementation. The dataset and implementation code for CrashChat are available at https://github.com/Liangkd/CrashChat.

Ke Li, Chenyu Zhang, Yuxin Ding et al.

Driving scenes are inherently heterogeneous and dynamic. Multi-attribute scene identification, as a high-level visual perception capability, provides autonomous vehicles (AVs) with essential contextual awareness to understand, reason through, and interact with complex driving environments. Although scene identification is best modeled as a multi-label classification problem via multitask learning, it faces two major challenges: the difficulty of acquiring balanced, comprehensively annotated datasets and the need to re-annotate all training data when new attributes emerge. To address these challenges, this paper introduces a novel deep learning method that integrates Knowledge Acquisition and Accumulation (KAA) with Consistency-based Active Learning (CAL). KAA leverages monotask learning on heterogeneous single-label datasets to build a knowledge foundation, while CAL bridges the gap between single- and multi-label data, adapting the foundation model for multi-label scene classification. An ablation study on the newly developed Driving Scene Identification (DSI) dataset demonstrates a 56.1% improvement over an ImageNet-pretrained baseline. Moreover, KAA-CAL outperforms state-of-the-art multi-label classification methods on the BDD100K and HSD datasets, achieving this with 85% less data and even recognizing attributes unseen during foundation model training. The DSI dataset and KAA-CAL implementation code are publicly available at https://github.com/KELISBU/KAA-CAL .

Xianbiao Hu, Qing Tang

Mobile and slow moving operations, such as striping, sweeping, bridge flushing and pothole patching, are critical for efficient and safe operation of the highway transportation system. A successfully implemented leader follower autonomous truck mounted attenuators system will eliminate all injuries to DOT employees in follow truck provided appropriate Statutory authority. The leader follower system design imposes more requirements to the lead truck drivers in order to ensure a safe and smooth system operation. The driver is now required to make driving decisions not only from the lead truck's perspective, but also consider the potential implications of his decisions to the follow truck. This project aims to develop a set of rules and clear instructions for ATMA system operation