An Exposure-Time-Aligned Primary-Path Architecture for Autonomous-Driving ECUs
On the map: End-to-end autonomous driving → End-to-end system architecture
Abstract (arXiv)
While end-to-end (E2E) autonomous driving has become the dominant research direction, production vehicles continue to rely on modular multi-NN pipelines for a non-trivial transitional period. The subject of this paper is the design of an architecture that, during this phase, supports a modular pipeline and an E2E path side by side and embeds a path for staged migration. Transplanted to a production SoC, egalitarian late fusion is compute-inefficient and offers no natural unit for staged E2E substitution. As an alternative, we propose three design principles: (i) Primary-Path, which selects a primary perception-to-planner chain and prioritizes its enclosure within a single SoC over the non-critical paths, (ii) Exposure-Time-Aligned, which propagates the primary sensor's exposure time τexp as a tag along the chain and event-drives the fusion node on matched τexp rather than a fixed cycle, and (iii) Co-Path Coexistence, which, building on (i) and (ii), lets an E2E output path co-run with the modular pipeline within the same τexp cycle. On a Dual-SoC production AD-ECU, the implementation achieves a camera-shutter to planner-output latency of 296 ms on average within a 350 ms design budget, with the modular pipeline and the E2E path coexisting within the shared τexp cycle.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 14 of its 21 references that are in the map. First posted on arXiv 9 Jun 2026.
Builds on
14 of 21 references.
Closest ancestors · 3
Same direction, newest first
- 2023End-to-end Autonomous Driving: Challenges and Frontiers29 Jun
- 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes17 May
- 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies28 Sep
Other references · 11
- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
- 2022Planning-oriented Autonomous Drivingcited by 1,087
- 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 622
- 2023DriveLM: Driving with Graph Visual Question Answeringcited by 536
- 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 490
- 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 379
- 2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
- 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 212
- 2024Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Drivingcited by 156
- 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
- 2018Baidu Apollo EM Motion Plannercited by 100
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- 2026The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
- 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
- 2024METDrive: Multi-modal End-to-end Autonomous Driving with Temporal Guidance
- 2024End-to-End Autonomous Driving through V2X Cooperation
- 2025FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
- 2022Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems