To perceive, humans use memory to fill in gaps caused by our limited visibility, whether due to occlusion or our narrow field of view. However, most 3D object detectors are limited to using sensor evidence from a short temporal window (0.1s-0.3s). In this work, we present a simple and effective add-on for enhancing any existing 3D object detector with long-term memory regardless of its sensor modality (e.g., LiDAR, camera) and network architecture. We propose a model to effectively align and fuse object proposals from a detector with object proposals from a memory bank of past predictions, exploiting trajectory forecasts to align proposals across time. We propose a novel schedule to train our model on temporal data that balances data diversity and the gap between training and inference. By applying our method to existing LiDAR and camera-based detectors on the Waymo Open Dataset (WOD) and Argoverse 2 Sensor (AV2) dataset, we demonstrate significant improvements in detection performance (+2.5 to +7.6 AP points). Our method attains the best performance on the WOD 3D detection leaderboard among online methods (excluding ensembles or test-time augmentation).

We present DIO, a flexible world model that can estimate the scene occupancy-flow from a sparse set of LiDAR observations, and decompose it into individual instances. DIO can not only complete instance shapes at the present time, but also forecast their occupancy-flow evolution over a future horizon. Thanks to its flexible prompt representation, DIO can take instance prompts from off-the-shelf models like 3D detectors, achieving state-of-the-art performance in the task of 4D semantic occupancy completion and forecasting on the Argoverse 2 dataset. Moreover, our world model can easily and effectively be transferred to downstream tasks like LiDAR point cloud forecasting, ranking first compared to all baselines in the Argoverse 4D occupancy forecasting challenge.

Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world—traditionally with object detections and trajectory predictions, or temporal bird's-eye-view (BEV) occupancy fields. However, these annotations are expensive and typically limited to a set of predefined categories that do not cover everything we might encounter on the road. Instead, we learn to perceive and forecast a continuous 4D (spatiotemporal) occupancy field with self-supervision from Li-DAR data. This unsupervised world model can be easily and effectively transferred to downstream tasks. We tackle point cloud forecasting by adding a lightweight learned renderer and achieve state-of-the-art performance in Argoverse 2, nuScenes, and KITTI. To further showcase its transferability, we fine-tune our model for BEV semantic occupancy forecasting and show that it outperforms the fully supervised state-of-the-art, especially when labeled data is scarce. Finally, when compared to prior state-of-the-art on spatio-temporal geometric occupancy prediction, our 4D world model achieves a much higher recall of objects from classes relevant to self-driving.

We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent explainable exploration and optimization capabilities of the Monte-Carlo Tree Search (MCTS), our method addresses complex decision-making in a dynamic environment. We propose a framework that combines MCTS with supervised learning, enabling the autonomous vehicle to effectively navigate through diverse scenarios. Experimental results demonstrate the effectiveness and adaptability of our approach, showcasing improved real-time decision-making and collision avoidance. This paper contributes to the field by providing a robust solution for motion planning in autonomous driving systems, enhancing their explainability and reliability. Code is available under https://github.com/raphychek/mbappe-nuplan.

Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Scene Graph Neural Networks with projected vectorized representations for multi-agent trajectory prediction. Projected vectorized representation models the traffic scene as a graph which is constructed by a set of vectors. These vectors represent agents, road network, and their spatial relative relationships. All relative features under this representation are both translation-and rotation-invariant. Based on this representation, TSGN captures the spatial-temporal features across agents, road network, interactions among them, and temporal dependencies of temporal traffic scenes. TSGN can predict multimodal future trajectories for all agents simultaneously, plausibly, and accurately. Mean-while, we propose a Hierarchical Lane Transformer for capturing interactions between agents and road network, which filters the surrounding road network and only keeps the most probable lane segments which could have an impact on the future behavior of the target agent. Without sacrificing the prediction performance, this greatly reduces the computational burden. Experiments show TSGN achieves state-of-the-art performance on the Argoverse motion forecasting benchmark.

We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or a 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Very large environments force competitors to store gigabytes of map data, whereas our method is very compact independently of the reference database size. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.

Information extraction (IE) from documents is an intensive area of research with a large set of industrial applications. Current state-of-the-art methods focus on scanned documents with approaches combining computer vision, natural language processing and layout representation. We propose to challenge the usage of computer vision in the case where both token style and visual representation are available (i.e native PDF documents). Our experiments on three real-world complex datasets demonstrate that using token style attributes based embedding instead of a raw visual embedding in LayoutLM model is beneficial. Depending on the dataset, such an embedding yields an improvement of 0.18% to 2.29% in weighted F1-score with a decrease of 30.7% in the final number of trainable parameters of the model, leading to an improvement in both efficiency and effectiveness.

In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future position probability distribution for a given agent in a traffic scene. This heatmap output yields an unconstrained 2D grid representation of agent future possible locations, allowing inherent multimodality and a measure of the uncertainty of the prediction. Our graph-oriented model avoids the high computation burden of representing the surrounding context as squared images and processing it with classical CNNs, but focuses instead only on the most probable lanes where the agent could end up in the immediate future. GOHOME reaches 2nd on Argoverse Motion Forecasting Benchmark on the Misskate6metric while achieving significant speed-up and memory burden diminution compared to Argoverse 1stplace method HOME. We also highlight that heatmap output enables multimodal ensembling and improve 1stplace MissRate6by more than 15% with our best ensemble on Argoverse. Finally, we evaluate and reach state-of-the-art performance on the other trajectory prediction datasets nuScenes and Interaction, demonstrating the generalizability of our method.

This paper presents a novel vehicle motion forecasting method based on multi-head attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-modal probability density functions of their positions. Its architecture uses multi-head attention to account for interactions between all vehicles, and long short-term memory layers for encoding and forecasting. It relies solely on vehicle position tracks, does not need maneuver definitions, and does not rasterize the scene as a spatial grid. This allows it to be more versatile than similar model while combining many forecasting capabilities, namely joint forecast with interactions, uncertainty estimation, and multi-modality. The resulting prediction likelihood outperforms state-of-the-art models on the same dataset.