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Chen Lv

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10 papers

2 author rows

EAAI Journal 2025 Journal Article

A domain generalization method for deploying driver distraction detection models to practical application scenarios

Lie Yang
Henglai Wei
Zhongxu Hu
Chen Lv

Driver distraction detection is crucial for reducing traffic accidents and enhancing driving safety. However, there is often a domain shift between data from real-world driving scenarios and the model training data, which limits the deployment of existing driver distraction detection models. To facilitate deployment of these models in practical application scenarios, a domain generalization method based on the contrastive language-image pretraining (CLIP) model and the constraint of center loss (DGCCL) is proposed in this paper. Firstly, the image encoder of the pre-trained CLIP model is adopted as the feature extraction module to improve the domain generalization ability of the proposed model. Furthermore, the constraint of center loss is introduced to promote the samples of different datasets to follow an approximately identical distribution in the feature space, thereby alleviating the domain shift problem. Additionally, the classification loss with additive angular margin penalty (AAMP) is introduced to further improve the cross-domain performance of the proposed model. In order to demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on three publicly available driver distraction detection datasets: AUC-DDD, State-Farm, and SAM-DD. The experimental results verify that our method can achieve much better performance than various well-known models in the cross-domain driver distraction detection tasks.

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ICRA Conference 2025 Conference Paper

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-Tuning

Zhiyu Huang
Xinshuo Weng
Maximilian Igl
Yuxiao Chen 0008
Yulong Cao
Boris Ivanovic
Marco Pavone 0001
Chen Lv

Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the Gen-Drive framework, which shifts from the traditional prediction and deterministic planning framework to a generation-then-evaluation planning paradigm. The framework employs a behavior diffusion model as a scene generator to produce diverse possible future scenarios, thereby enhancing the capability for joint interaction reasoning. To facilitate decision-making, we propose a scene evaluator (reward) model, trained with pairwise preference data collected through VLM assistance, thereby reducing human workload and enhancing scalability. Furthermore, we utilize an RL fine-tuning framework to improve the generation quality of the diffusion model, rendering it more effective for planning tasks. We conduct training and closed-loop planning tests on the nuPlan dataset, and the results demonstrate that employing such a generation-then-evaluation strategy outperforms other learning-based approaches. Additionally, the fine-tuned generative driving policy shows significant enhancements in planning performance. We further demonstrate that utilizing our learned reward model for evaluation or RL fine-tuning leads to better planning performance compared to relying on human-designed rewards. Project website: https://mczhi.github.io/GenDrive.

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IROS Conference 2025 Conference Paper

TRACER: Thrust Auto-calibration and Ground Effect Estimation Using Onboard Force Sensitive Resistor Array for Multirotors

Baichuan Lou
Lingxiao Deng
Yuan Ji
Yanxin Zhou
Chen Lv

Auto-calibration of the rotor thrust coefficient and estimation of ground effect are both challenging aspects of multirotor dynamics control and planning. Conventional approaches address these issues separately and typically rely on experimental rigs for bench testing. In this paper, we propose a low-cost onboard sensor array and a well-designed unified algorithm to enable fast auto-calibration of the rotor thrust coefficient alongside ground effect estimation (TRACER). Our sensor array consists of four force-sensitive resistors compactly placed under the quadrotor landing gear to measure contact force during the lift-off phase, capturing changes in thrust. The joint calibration and estimation problem is formulated to rapidly decouple ground effect influence from free-air rotor thrust based on sensor readings. Furthermore, our approach is adaptable to various flight control input formats (duty cycle, rotor throttle, or rpm), ensuring general applicability across different multirotor operations. Experimental results demonstrate that the proposed method provides reliable joint calibration and estimation of rotor thrust and ground effect in a short lift-off process, achieving less than 10% mean absolute percentage error compared to the ground truth.

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NeurIPS Conference 2025 Conference Paper

V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception

Lei Yang
Xinyu Zhang
Jun Li
Chen Wang
Jiaqi Ma
Zhiying Song
Tong Zhao
Ziying Song

Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on cameras and LiDAR, neglecting 4D Radar—a sensor used in single-vehicle autonomous driving to provide robust perception in adverse weather conditions. In this paper, to bridge the gap created by the absence of 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large-scale, real-world multi-modal dataset featuring 4D Radar. V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data encompasses sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as various typical challenging scenarios. The dataset consists of 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, including 350K annotated boxes across five categories. To support various research domains, we have established V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. Furthermore, we provide comprehensive benchmarks across these three sub-datasets.

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ICRA Conference 2024 Conference Paper

DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving

Zhiyu Huang
Péter Karkus
Boris Ivanovic
Yuxiao Chen 0008
Marco Pavone 0001
Chen Lv

Motion prediction and cost evaluation are vital components in the decision-making system of autonomous vehicles. However, existing methods often ignore the importance of cost learning and treat them as separate modules. In this study, we employ a tree-structured policy planner and propose a differentiable joint training framework for both ego-conditioned prediction and cost models, resulting in a direct improvement of the final planning performance. For conditional prediction, we introduce a query-centric Transformer model that performs efficient ego-conditioned motion prediction. For planning cost, we propose a learnable context-aware cost function with latent interaction features, facilitating differentiable joint learning. We validate our proposed approach using the real-world nuPlan dataset and its associated planning test platform. Our framework not only matches state-of-the-art planning methods but outperforms other learning-based methods in planning quality, while operating more efficiently in terms of runtime. We show that joint training delivers significantly better performance than separate training of the two modules. Additionally, we find that tree-structured policy planning outperforms the conventional single-stage planning approach. Code is available: https://github.com/MCZhi/DTPP.

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NeurIPS Conference 2024 Conference Paper

Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

Haochen Liu
Li Chen
Yu Qiao
Chen Lv
Hongyang Li

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction. Current dense and sparse behavioral representations struggle with inefficiency and inconsistency in multi-agent modeling, leading to instability of collective behavioral patterns when integrating prediction and planning (IPP). To address this, we initiate a topological formation that serves as a compliant behavioral foreground to guide downstream trajectory generations. Specifically, we introduce Behavioral Topology (BeTop), a pivotal topological formulation that explicitly represents the consensual behavioral pattern among multi-agent future. BeTop is derived from braid theory to distill compliant interactive topology from multi-agent future trajectories. A synergistic learning framework (BeTopNet) supervised by BeTop facilitates the consistency of behavior prediction and planning within the predicted topology priors. Through imitative contingency learning, BeTop also effectively manages behavioral uncertainty for prediction and planning. Extensive verification on large-scale real-world datasets, including nuPlan and WOMD, demonstrates that BeTop achieves state-of-the-art performance in both prediction and planning tasks. Further validations on the proposed interactive scenario benchmark showcase planning compliance in interactive cases. Code and model is available at https: //github. com/OpenDriveLab/BeTop.

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NeurIPS Conference 2024 Conference Paper

Variational Delayed Policy Optimization

Qingyuan Wu
Simon S. Zhan
Yixuan Wang
Yuhui Wang
Chung-Wei Lin
Chen Lv
Qi Zhu
Chao Huang

In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL). Whereas, state-of-the-art (SOTA) RL techniques with Temporal-Difference (TD) learning frameworks commonly suffer from learning inefficiency, due to the significant expansion of the augmented state space with the delay. To improve the learning efficiency without sacrificing performance, this work novelly introduces Variational Delayed Policy Optimization (VDPO), reforming delayed RL as a variational inference problem. This problem is further modelled as a two-step iterative optimization problem, where the first step is TD learning in the delay-free environment with a small state space, and the second step is behaviour cloning which can be addressed much more efficiently than TD learning. We not only provide a theoretical analysis of VDPO in terms of sample complexity and performance, but also empirically demonstrate that VDPO can achieve consistent performance with SOTA methods, with a significant enhancement of sample efficiency (approximately 50\% less amount of samples) in the MuJoCo benchmark.

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ICRA Conference 2023 Conference Paper

Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving

Haochen Liu
Zhiyu Huang
Chen Lv

Forecasting the future states of surrounding traffic participants is a crucial capability for autonomous vehicles. The recently proposed occupancy flow field prediction introduces a scalable and effective representation to jointly predict surrounding agents' future motions in a scene. However, the challenging part is to model the underlying social interactions among traffic agents and the relations between occupancy and flow. Therefore, this paper proposes a novel Multi-modal Hierarchical Transformer network that fuses the vectorized (agent motion) and visual (scene flow, map, and occupancy) modalities and jointly predicts the flow and occupancy of the scene. Specifically, visual and vector features from sensory data are encoded through a multi-stage Transformer module and then a late-fusion Transformer module with temporal pixel-wise attention. Importantly, a flow-guided multi-head self-attention (FG-MSA) module is designed to better aggregate the information on occupancy and flow and model the mathematical relations between them. The proposed method is comprehensively validated on the Waymo Open Motion Dataset and compared against several state-of-the-art models. The results reveal that our model with much more compact architecture and data inputs than other methods can achieve comparable performance. We also demonstrate the effectiveness of incorporating vectorized agent motion features and the proposed FG-MSA module. Compared to the ablated model without the FG-MSA module, which won 2 nd place in the 2022 Waymo Occupancy and Flow Prediction Challenge, the current model shows better separability for flow and occupancy and further performance improvements.

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ICRA Conference 2022 Conference Paper

Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving

Zhiyu Huang
Xiaoyu Mo
Chen Lv

Predicting the behaviors of other agents on the road is critical for autonomous driving to ensure safety and efficiency. However, the challenging part is how to represent the social interactions between agents and output different possible trajectories with interpretability. In this paper, we introduce a neural prediction framework based on the Transformer structure to model the relationship among the interacting agents and extract the attention of the target agent on the map waypoints. Specifically, we organize the interacting agents into a graph and utilize the multi-head attention Transformer encoder to extract the relations between them. To address the multi-modality of motion prediction, we propose a multi-modal attention Transformer encoder, which modifies the multi-head attention mechanism to multi-modal attention, and each predicted trajectory is conditioned on an independent attention mode. The proposed model is validated on the Argoverse motion forecasting dataset and shows state-of-the-art prediction accuracy while maintaining a small model size and a simple training process. We also demonstrate that the multi-modal attention module can automatically identify different modes of the target agent's attention on the map, which improves the interpretability of the model.

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IS Journal 2022 Journal Article

Xsickness in Intelligent Mobile Spaces and Metaverses

Ruichen Tan
Ruiyang Gao
Wenbo Li
Kai Cao
Ying Li
Chen Lv
Fei-Yue Wang
Dongpu Cao

Motion sickness is known to be a common problem that influences the comfort and work efficiency of human beings during their daily lives. With the proliferation of increasingly intelligent systems, the detection and mitigation of motion sickness will face more opportunities along with bigger challenges. On the one hand, the technology for integrated sensors in the intelligent system will provide more accurate and efficient methods for motion sickness detection. However, on the other hand, since cyber-physical systems have been gaining increasing concerns in the past two decades, the cyber-physical-social systems introduce and augment the social characteristics of such systems. The interactions between physical space and cyber space increase the chance of sensory conflicts when people use intelligent systems, such as traveling in intelligent cockpits or using metaverse-related virtual reality devices. The multimodal interaction methods and larger screens will cause more sensory conflicts. The symptoms will be more severe compared to traditional motion sickness. In this article, the classifications are first introduced based on the causes of motion sickness. A new type of multifactorial motion sickness (Xsickness) is discussed, which is foreseeable to be common with intelligent development. Then, the current state-of-the-art detection methods for motion sickness and cybersickness are summarized and theoretical methods for Xsickness detection are discussed. Finally, the mitigation methods based on motion reduction and four means of human perception are discussed and the innovative mitigation methods based on the intelligent system are also introduced.

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