CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
- URL: http://arxiv.org/abs/2604.14454v1
- Date: Wed, 15 Apr 2026 22:15:50 GMT
- Title: CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
- Abstract summary: CooperDrive is a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions.<n>CooperDrive reuses detector Bird's-Eye View (BEV) features to estimate accurate vehicle poses without additional heavy encoders.<n>On the planning side, CooperDrive leverages the expanded object set to anticipate potential conflicts earlier and adjust speed and trajectory proactively.
- Score: 11.113244744008844
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed reactions can increase collision risk. We propose CooperDrive, a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions. CooperDrive offers two key advantages: (i) each vehicle retains its native perception, localization, and planning stack, and (ii) a lightweight object-level sharing and fusion strategy bridges perception and planning. Specifically, CooperDrive reuses detector Bird's-Eye View (BEV) features to estimate accurate vehicle poses without additional heavy encoders, thereby reconstructing BEV representations and feeding the planner with low latency. On the planning side, CooperDrive leverages the expanded object set to anticipate potential conflicts earlier and adjust speed and trajectory proactively, thereby transforming reactive behaviors into predictive and safer driving decisions. Real-world closed-loop tests at occlusion-heavy NLOS intersections demonstrate that CooperDrive increases reaction lead time, minimum time-to-collision (TTC), and stopping margin, while requiring only 90 kbps bandwidth and maintaining an average end-to-end latency of 89 ms.
Related papers
- VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
arXiv Detail & Related papers (2026-09-02T11:32:52Z) - End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation [59.188006170485046]
E2E-CDiff is an end-to-end conditional diffusion framework for controllable and realistic scenario generation.<n>Differentiable guidance regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors.<n>E2E-CDiff also performs competitively as a learning-based ego planner.
arXiv Detail & Related papers (2026-07-21T02:13:25Z) - Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
arXiv Detail & Related papers (2026-05-18T08:45:24Z) - ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving [63.980630608984605]
This paper presents ADV-0, a closed-loop min-max optimization framework that treats the interaction between driving policy (defender) and adversarial agent (attacker) as a zero-sum Markov game.<n>To make this tractable, we cast dynamic adversary evolution as iterative preference learning, efficiently approximating this optimum and offering an algorithm-agnostic solution to the game.<n> Experiments indicate that it effectively exposes diverse safety-critical failures and greatly enhances the generalizability of both learned policies and motion planners against unseen long-tail risks.
arXiv Detail & Related papers (2026-03-16T12:58:31Z) - Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
arXiv Detail & Related papers (2025-12-08T15:56:18Z) - ExpertAD: Enhancing Autonomous Driving Systems with Mixture of Experts [13.846892166111646]
ExpertAD is a novel framework that enhances the performance of autonomous driving systems.<n>Our experiments show that ExpertAD reduces average collision rates by up to 20% and inference latency by 25% compared to prior methods.
arXiv Detail & Related papers (2025-11-13T13:14:17Z) - SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving [5.343552118560704]
SpaRC-AD is a query-based end-to-end camera-radar fusion framework for planning-oriented autonomous driving.<n>Our method achieves strong improvements over the state-of-the-art vision-only baselines across multiple autonomous driving tasks.
arXiv Detail & Related papers (2025-08-14T12:02:41Z) - Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance [12.513296074529727]
This paper proposes the Real-time Edge-based Autonomous Co-pilot Trajectory planner (REACT) for autonomous driving.<n>REACT is a V2X-integrated trajectory optimization framework for AD based on a fine-tuned lightweight Vision-Language Model (VLM)<n> evaluated on the DeepAccident benchmark, REACT achieves state-of-the-art performance, a 77% collision rate reduction, a 48.2% Video Panoptic Quality (VPQ), and a 0.57-second inference latency on the Jetson AGX Orin.
arXiv Detail & Related papers (2025-08-01T20:16:04Z) - Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training [64.16445087751039]
Hydra-NeXt is a novel multi-branch planning framework that unifies trajectory prediction, control prediction, and a trajectory refinement network in one model.<n> Hydra-NeXt surpasses the previous state-of-the-art by 22.98 DS and 17.49 SR, marking a significant advancement in autonomous driving.
arXiv Detail & Related papers (2025-03-15T07:42:27Z) - DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
arXiv Detail & Related papers (2024-09-15T15:55:24Z) - Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections [12.812518632907771]
We present a novel framework that detects preemptively collisions at urban crossroads.
We exploit the Multi-access Edge Computing platform of 5G networks.
arXiv Detail & Related papers (2024-04-22T18:45:40Z) - Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of
Traffic Agents in Risky Driving Videos [20.923004256768635]
Fusion-Gated Recurrent Unit (Fusion-GRU) is a novel encoder-decoder architecture for future bounding box localization.
The proposed method is evaluated on two publicly available datasets, ROL and HEV-I.
arXiv Detail & Related papers (2023-08-12T18:35:59Z) - Deep Structured Reactive Planning [94.92994828905984]
We propose a novel data-driven, reactive planning objective for self-driving vehicles.
We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
arXiv Detail & Related papers (2021-01-18T01:43:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.