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Overview: While autonomous cars are rapidly becoming a reality, it remains a challenge to scalably deploy them across geographies and conditions. Our full-stack autonomy solutions include perception, prediction, planning, simulation and DevOps that leverage the latest advances in generative AI, neural rendering, large language models, diffusion models and transformers.
Overview: Our methods such as DESIRE, SMART and DAC achieve various capabilities such as diversity, scene consistency, constant-time inference and multimodality that adheres to lane geometries and driving rules.
Overview: We develop AI solutions to assure customers that private information is not leaked at any stage of the data lifecycle. Our differential privacy method guarantees that an adversary cannot decipher training data from model outputs.
Overview: We develop novel computational cameras that allow computer vision analysis even in sensitive environments like hospitals or smart homes. Our key innovation is a camera that removes private information.
Overview: We propose advances that address two key challenges in future trajectory prediction: (i) multi-modality in both training data and predictions and (ii) constant time inference regardless of number of agents. Existing trajectory predictions are fundamentally limited by lack of diversity in training data, which is difficult to acquire with sufficient coverage of possible modes.
Overview: Our work addresses two key challenges in trajectory prediction: (i) learning multimodal outputs and (ii) improving predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performance using multi-choice learning objectives like winner-takes-all (WTA), but they highly depend on their initialization to provide diverse outputs.
Our HorizonWeaver software edits driving scenes with instruction-guided AI, adding traffic, changing weather, and generalizing to unseen roads, all while preserving the safety-critical details that keep autonomous vehicle testing honest.
LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D scene graph, decomposing the scene into a static background and dynamic object nodes. To enable fine-grained editing and
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guided image editors, trained on object-centric or artistic data, struggle with dense, safety-critical driving layouts. We propose
Controllable driving scene generation is critical for realistic and scalable autonomous driving simulation, yet existing approaches struggle to jointly achieve photorealism and precise control. We introduce HorizonForge, a unified framework that reconstructs scenes as editable Gaussian Splats and Meshes,
NEC Labs America headed to Denver for CVPR 2026, one of the most prestigious gatherings in computer vision, machine learning, and pattern recognition. The IEEE/CVF Conference on Computer Vision and Pattern Recognition brought innovators from around the world to share breakthroughs.
HorizonForge introduces a new approach to driving scene generation, enabling precise control over both vehicle behavior and identity. By allowing arbitrary trajectories and flexible vehicle insertion, it creates realistic, scalable simulations for autonomous driving, digital twins, and advanced AI development.
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guided image editors, trained on object-centric or artistic data, struggle with dense, safety-critical driving layouts. We propose
Controllable driving scene generation is critical for realistic and scalable autonomous driving simulation, yet existing approaches struggle to jointly achieve photorealism and precise control. We introduce HorizonForge, a unified framework that reconstructs scenes as editable Gaussian Splats and Meshes,
Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj, a language-conditioned scene-diffusion model that simulates the joint behavior of all agents in traffic scenarios. By conditioning
Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail safety-critical traffic scenarios. However, traditional methods for generating such scenarios often fall short in terms of controllability and realism; they also neglect the dynamics of agent interactions.
Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless
Data augmentation techniques, such as image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches,
Lensless cameras multiplex the incoming light before it is recorded by the sensor. This ability to multiplex the incoming light has led to the development of ultra-thin, high-speed, and single-shot 3D imagers. Recently, there have been various attempts at demonstrating another useful aspect of lensless
Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios. Learning-based planners suffer from overfitting and poor long-tail performance. On
Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail traffic scenarios. Traditional methods for generating safety-critical scenarios often fall short in realism and controllability. Furthermore, these techniques generally neglect the dynamics of agent
NEC Labs America is proud to be a Silver Sponsor for NeurIPS 2023 in New Orleans from December 10-16. Visit our booth to meet our team and learn about our intern opportunities in machine learning, data science, media analytics and integrated systems. Also, our Vijay Kumar.B.G, Samuel Schulter & Manmohan
Modern computer vision services often require users to share raw feature descriptors with an untrusted server. This presents an inherent privacy risk, as raw descriptors may be used to recover the source images from which they were extracted. To address this issue, researchers recently proposed privatizing
With over a billion sold each year, cameras are not only becoming ubiquitous, but are driving progress in a wide range of domains such as mixed reality, robotics, and more. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments
Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong
Differentially Private Federated Learning (DPFL) is an emerging field with many applications. Gradient averaging-based DPFL methods require costly communication rounds and hardly work with large capacity models due to the explicit dimension dependence in its added noise. In this work, inspired by knowledge
We propose advances that address two key challenges in future trajectory prediction: (i) multimodality in both training data and predictions and (ii) constant time inference regardless of number of agents. Existing trajectory predictions are fundamentally limited by lack of diversity in training data,
