CVPR 2024 Papers

We present a highly scalable self-training framework for incrementally adapting vision-based end-to-end autonomous driving policies in a semi-supervised manner i.e. over a continual stream of incoming video data. To facilitate large-scale model training (e.g. open web or unlabeled data) we do not assume access to ground-truth labels and instead estimate pseudo-label policy targets for each video. Our framework comprises three key components: knowledge distillation a sample purification module and an exploration and knowledge retention mechanism. First given sequential image frames we pseudo-label the data and estimate uncertainty using an ensemble of inverse dynamics models. The uncertainty is used to select the most informative samples to add to an experience replay buffer. We specifically select high-uncertainty pseudo-labels to facilitate the exploration and learning of new and diverse driving skills. However in contrast to prior work in continual learning that assumes ground-truth labeled samples the uncertain pseudo-labels can introduce significant noise. Thus we also pair the exploration with a label refinement module which makes use of consistency constraints to re-label the noisy exploratory samples and effectively learn from diverse data. Trained as a complete never-ending learning system we demonstrate state-of-the-art performance on training from domain-changing data as well as millions of images from the open web.

To advance research in learning-based defogging algorithms various synthetic fog datasets have been developed. However exsiting datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process closely aligning with real-world image capture methods. Based on this pipeline we present a new synthetic fog dataset named SynFog which features both sky light and active lighting conditions as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images.

Conventional image sensors digitize high-resolution images at fast frame rates producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor fabrication emerging sensor--processors offer programmability and processing capabilities directly on the sensor. We exploit these capabilities by developing an efficient recurrent neural network architecture PixelRNN that encodes spatio-temporal features on the sensor using purely binary operations. PixelRNN reduces the amount of data to be transmitted off the sensor by factors up to 256 compared to the raw sensor data while offering competitive accuracy for hand gesture recognition and lip reading tasks. We experimentally validate PixelRNN using a prototype implementation on the SCAMP-5 sensor--processor platform.

Single image depth estimation is a foundational task in computer vision and generative modeling. However prevailing depth estimation models grapple with accommodating the increasing resolutions commonplace in today's consumer cameras and devices. Existing high-resolution strategies show promise but they often face limitations ranging from error propagation to the loss of high-frequency details. We present PatchFusion a novel tile-based framework with three key components to improve the current state of the art: (1) A patch-wise fusion network that fuses a globally-consistent coarse prediction with finer inconsistent tiled predictions via high-level feature guidance (2) A Global-to-Local (G2L) module that adds vital context to the fusion network discarding the need for patch selection heuristics and (3) A Consistency-Aware Training (CAT) and Inference (CAI) approach emphasizing patch overlap consistency and thereby eradicating the necessity for post-processing. Experiments on UnrealStereo4K MVS-Synth and Middleburry 2014 demonstrate that our framework can generate high-resolution depth maps with intricate details. PatchFusion is independent of the base model for depth estimation. Notably our framework built on top of SOTA ZoeDepth brings improvements for a total of 17.3% and 29.4% in terms of the root mean squared error (RMSE) on UnrealStereo4K and MVS-Synth respectively.

Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.

We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios.

End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However the oversized neural networks render them impractical for deployment on resource-constrained systems which unavoidably requires more computational time and resources during reference. To handle this knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model. Nevertheless how to apply knowledge distillation to compress motion planners has not been explored so far. In this paper we propose PlanKD the first knowledge distillation framework tailored for compressing end-to-end motion planners. First considering that driving scenes are inherently complex often containing planning-irrelevant or even noisy information transferring such information is not beneficial for the student planner. Thus we design an information bottleneck based strategy to only distill planning-relevant information rather than transfer all information indiscriminately. Second different waypoints in an output planned trajectory may hold varying degrees of importance for motion planning where a slight deviation in certain crucial waypoints might lead to a collision. Therefore we devise a safety-aware waypoint-attentive distillation module that assigns adaptive weights to different waypoints based on the importance to encourage the student to accurately mimic more crucial waypoints thereby improving overall safety. Experiments demonstrate that our PlanKD can boost the performance of smaller planners by a large margin and significantly reduce their reference time.

Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation enabling testing of AD systems and as an advanced training data augmentation technique. However existing methods often require long training times dense semantic supervision or lack generalizability. This in turn hinders the application of NeRFs for AD at scale. In this paper we propose \modelname a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design extensive sensor modeling for both camera and lidar -- including rolling shutter beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets achieving state-of-the-art performance across the board. To encourage further development we openly release the NeuRAD source code at https://github.com/georghess/NeuRAD.

Despite significant recent progress in the field of autonomous driving modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand large language models (LLM) have shown impressive reasoning capabilities that approach "Artificial General Intelligence". On the other hand previous autonomous driving methods tend to rely on limited-format inputs (e.g. sensor data and navigation waypoints) restricting the vehicle's ability to understand language information and interact with humans. To this end this paper introduces LMDrive a novel language-guided end-to-end closed-loop autonomous driving framework. LMDrive uniquely processes and integrates multi-modal sensor data with natural language instructions enabling interaction with humans and navigation software in realistic instructional settings. To facilitate further research in language-based closed-loop autonomous driving we also publicly release the corresponding dataset which includes approximately 64K instruction-following data clips and the LangAuto benchmark that tests the system's ability to handle complex instructions and challenging driving scenarios. Extensive closed-loop experiments are conducted to demonstrate LMDrive's effectiveness. To the best of our knowledge we're the very first work to leverage LLMs for closed-loop end-to-end autonomous driving. Code is available at https://github.com/opendilab/LMDrive

End-to-end autonomous driving recently emerged as a promising research direction to target autonomy from a full-stack perspective. Along this line many of the latest works follow an open-loop evaluation setting on nuScenes to study the planning behavior. In this paper we delve deeper into the problem by conducting thorough analyses and demystifying more devils in the details. We initially observed that the nuScenes dataset characterized by relatively simple driving scenarios leads to an under-utilization of perception information in end-to-end models incorporating ego status such as the ego vehicle's velocity. These models tend to rely predominantly on the ego vehicle's status for future path planning. Beyond the limitations of the dataset we also note that current metrics do not comprehensively assess the planning quality leading to potentially biased conclusions drawn from existing benchmarks. To address this issue we introduce a new metric to evaluate whether the predicted trajectories adhere to the road. We further propose a simple baseline able to achieve competitive results without relying on perception annotations. Given the current limitations on the benchmark and metrics we suggest the community reassess relevant prevailing research and be cautious about whether the continued pursuit of state-of-the-art would yield convincing and universal conclusions. Code and models are available at https://github.com/NVlabs/BEV-Planner.

Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary lane divider pedestrian crossing etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However this pipeline has limitations in obtaining element-level information and handling element-level failures e.g. erroneous element shape or entanglement between elements. To tackle the above issues we propose a simple yet effective HybrId framework named HIMap to sufficiently learn and interact both point-level and element-level information. Concretely we introduce a hybrid representation called HIQuery to represent all map elements and propose a point-element interactor to interactively extract and encode the hybrid information of elements e.g. point position and element shape into the HIQuery. Additionally we present a point-element consistency constraint to enhance the consistency between the point-level and element-level information. Finally the output point-element integrated HIQuery can be directly converted into map elements' class point coordinates and mask. We conduct extensive experiments and consistently outperform previous methods on both nuScenes and Argoverse2 datasets. Notably our method achieves 77.8 mAP on the nuScenes dataset remarkably superior to previous SOTAs by 8.3 mAP at least.

In this paper we explore a novel challenging generation task i.e. Handwritten Mathematical Expression Generation (HMEG) from symbolic sequences. Since symbolic sequences are naturally graph-structured data we formulate HMEG as a graph-to-image (G2I) generation problem. Unlike the generation of natural images HMEG requires critic layout clarity for synthesizing correct and recognizable formulas but has no real masks available to supervise the learning process. To alleviate this challenge we propose a novel end-to-end G2I generation pipeline (i.e. graph - layout - mask - image) which requires no real masks or nondifferentiable alignment between layouts and masks. Technically to boost the capacity of predicting detailed relations among adjacent symbols we propose a Less-is-More (LiM) learning strategy. In addition we design a differentiable layout refinement module which maps bounding boxes to pixel-level soft masks so as to further alleviate ambiguous layout areas. Our whole model including layout prediction mask refinement and image generation can be jointly optimized in an end-to-end manner. Experimental results show that our model can generate high-quality HME images and outperforms previous generative methods. Besides a series of ablations study demonstrate effectiveness of the proposed techniques. Finally we validate that our generated images promisingly boosts the performance of HME recognition models through data augmentation. Our code and results are available at: https://github.com/AiArt-HDU/HMEG.

In this paper we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower the generalization ability of our model we acquire massive data from the web and pair it with diverse and high-quality text descriptions. The resultant dataset accumulates over 2000 hours of driving videos spanning areas all over the world with diverse weather conditions and traffic scenarios. Inheriting the merits from recent latent diffusion models our model dubbed GenAD handles the challenging dynamics in driving scenes with novel temporal reasoning blocks. We showcase that it can generalize to various unseen driving datasets in a zero-shot manner surpassing general or driving-specific video prediction counterparts. Furthermore GenAD can be adapted into an action-conditioned prediction model or a motion planner holding great potential for real-world driving applications.

We introduce GaussianAvatars a new method to create photorealistic head avatars that are fully controllable in terms of expression pose and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model e.g. through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance we show reenactments from a driving video where our method outperforms existing works by a significant margin.

While behavior cloning has recently emerged as a highly successful paradigm for autonomous driving humans rarely learn to perform complex tasks such as driving via imitation or behavior cloning alone. In contrast learning in humans often involves additional detailed guidance throughout the interactive learning process i.e. where feedback often via language provides detailed information as to which part of their trial was performed incorrectly or suboptimally and why. Motivated by this observation we introduce an efficient feedback-based framework for improving behavior-cloning-based training of sensorimotor driving agents. Our key insight is to leverage recent advances in Large Language Models (LLMs) to provide corrective fine-grained feedback regarding the underlying reason behind driving prediction failures. Moreover our introduced network architecture is efficient enabling the first sensorimotor end-to-end training and evaluation of LLM-based driving models. The resulting agent achieves state-of-the-art performance in open-loop evaluation on nuScenes outperforming prior state-of-the-art by over 8.1% and 57.1% in accuracy and collision rate respectively. In CARLA our camera-based agent improves by 16.6% in driving score over prior LIDAR-based approaches.

Recently temporal action detection (TAD) has seen significant performance improvement with end-to-end training. However due to the memory bottleneck only models with limited scales and limited data volumes can afford end-to-end training which inevitably restricts TAD performance. In this paper we reduce the memory consumption for end-to-end training and manage to scale up the TAD backbone to 1 billion parameters and the input video to 1536 frames leading to significant detection performance. The key to our approach lies in our proposed temporal-informative adapter (TIA) which is a novel lightweight module that reduces training memory. Using TIA we free the humongous backbone from learning to adapt to the TAD task by only updating the parameters in TIA. TIA also leads to better TAD representation by temporally aggregating context from adjacent frames throughout the backbone. We evaluate our model across four representative datasets. Owing to our efficient design we are able to train end-to-end on VideoMAEv2-giant and achieve 75.4% mAP on THUMOS14 being the first end-to-end model to outperform the best feature-based methods.

The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end transformer based model that directly ingests an input video and outputs tubelets -- a sequence of bounding boxes and the action classes at each frame. Our flexible model can be trained with either sparse bounding-box supervision on individual frames or full tubelet annotations. And in both cases it predicts coherent tubelets as the output. Moreover our end-to-end model requires no additional pre-processing in the form of proposals or post-processing in terms of non-maximal suppression. We perform extensive ablation experiments and significantly advance the state-of-the-art on five different spatio-temporal action localisation benchmarks with both sparse keyframes and full tubelet annotations.

State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules. In contrast to previous approaches that rely on a unified grid to represent the belief state of the scene we propose dedicated representations to disentangle dynamic agents and static scene elements. This allows us to explicitly compensate for the effect of both ego and object motion between consecutive time steps and to flexibly propagate the belief state through time. Furthermore dynamic objects can not only attend to the input camera images but also directly benefit from the inferred static scene structure via a novel dynamic-static cross-attention. Extensive experiments on the challenging nuScenes benchmark demonstrate the benefits of the proposed dual-stream design especially for modelling highly dynamic agents in the scene and highlight the improved temporal consistency of our approach. Our method titled DualAD not only outperforms independently trained single-task networks but also improves over previous state-of-the-art end-to-end models by a large margin on all tasks along the functional chain of driving.