SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
CARLA: An Open Urban Driving Simulator
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform's utility for autonomous driving research. The supplementary video can be viewed at https://youtu.be/Hp8Dz-Zek2E
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representative citing papers
EgoDyn-Bench reveals a perception bottleneck in vision-centric foundation models: ego-motion logic derives from language while visual input adds negligible signal, with explicit trajectories restoring consistency.
Presents a geo-data-driven workflow that generates lane-level HD maps from open shapefile road data and verifies them via executable constraints derived from automated driving specifications and road design guidelines.
SynthCity is a 367.9M point synthetic full-colour Mobile Laser Scanning point cloud with per-point labels from nine categories, generated in Blender for an urban environment.
The paper presents robosuite v1.5, a MuJoCo-based modular simulation framework with benchmark environments for reproducible robot learning research.
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
citing papers explorer
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SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
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EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
EgoDyn-Bench reveals a perception bottleneck in vision-centric foundation models: ego-motion logic derives from language while visual input adds negligible signal, with explicit trajectories restoring consistency.
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Geo-Data-Driven HD Map Generation Workflow with Integrated Reference-Free Constraint-Based Verification
Presents a geo-data-driven workflow that generates lane-level HD maps from open shapefile road data and verifies them via executable constraints derived from automated driving specifications and road design guidelines.
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SynthCity: A large scale synthetic point cloud
SynthCity is a 367.9M point synthetic full-colour Mobile Laser Scanning point cloud with per-point labels from nine categories, generated in Blender for an urban environment.
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robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
The paper presents robosuite v1.5, a MuJoCo-based modular simulation framework with benchmark environments for reproducible robot learning research.
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Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
- Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty