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MP3: A Unified Model to Map, Perceive, Predict and Plan
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High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with centimeter-level accuracy. Being able to drive without an HD map would be very beneficial to scale self-driving solutions as well as to increase the failure tolerance of existing ones (e.g., if localization fails or the map is not up-to-date). Towards this goal, we propose MP3, an end-to-end approach to mapless1driving where the input is raw sensor data and a high-level command (e.g., turn left at the intersection). MP3 predicts intermediate representations in the form of an online map and the current and future state of dynamic agents, and exploits them in a novel neural motion planner to make interpretable decisions taking into account uncertainty. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations, as well as when compared to an expert driver in a large-scale real-world dataset.
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From Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving (2024) · cites this paper as [58]
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MapPrior: Bird’s-Eye View Map Layout Estimation with Generative Models
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OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge
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Encoding the Intrinsic Interaction Information for Vehicle Trajectory Prediction
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Occupancy Prediction-Guided Neural Planner for Autonomous Driving
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UnO: Unsupervised Occupancy Fields for Perception and Forecasting
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MPNP: Multi-Policy Neural Planner for Urban Driving
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Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization
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A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN Training
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Network-Level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
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TIP: Task-Informed Motion Prediction for Intelligent Vehicles
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Roadside Sensor Systems for Vulnerable Road User Protection: A Review of Methods and Applications
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Safety-Oriented Pedestrian Occupancy Forecasting
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Self-citationMoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
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Human-Centric Intelligent Driving: Collaborating with the Driver to Improve Safety
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Motion Planning Using Feasible and Smooth Tree for Autonomous Driving
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Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection
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OFMPNet: Deep end-to-end model for occupancy and flow prediction in urban environment
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Multi-Granular Transformer for Motion Prediction with LiDAR
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Metadata
- Authors
- Casas, S., Sadat, A., & Urtasun, R.
- Affiliations
- University of Toronto (CA)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 14398-14407
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3125926538
- Cited by
- 228
- References
- 61
- Citations by year
- 2026: 17 · 2025: 49 · 2024: 59 · 2023: 59 · 2022: 34 · 2021: 10
- FWCI
- 40.11
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous vehicles, end-to-end driving, online maps, decision making under uncertainty, high-definition maps, closed-loop simulation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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