An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Hanlin Tian; Jose Escribano; Leandro Parada; Panagiotis Angeloudis

REVIEW 4 major objections 5 minor 34 references

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that a MAPPO-based policy sharing compressed LiDAR features over V2V networks lowers collisions in occluded intersections to roughly 2-3%, outperforming independent RL and early fusion while staying within bandwidth…

desk verdict A plausible MARL+V2V feature-sharing pipeline with a real novelty story, but the headline comparisons lack the statistical and training-detail support to back 'consistent superiority.' read the letter →

arxiv 2412.08562 v1 pith:JAWG6DGC submitted 2024-12-11 cs.RO cs.MA

Leandro Parada , Hanlin Tian , Jose Escribano , Panagiotis Angeloudis This is my paper · ORCID

classification cs.ROcs.MA

keywords connectedautonomousvehiclesV2Vcommunicationoccludedintersectionmulti-agentreinforcementlearningMAPPOLiDARfeaturesharingintermediatefusiondrivingsafety

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that vehicles approaching blind intersections can learn to avoid crashes by exchanging compact LiDAR features over a local V2V network and training a shared control policy with multi-agent reinforcement learning. The method uses MAPPO, compresses LiDAR point clouds into small feature tensors, broadcasts them to nearby connected vehicles, and feeds the aggregated features into an actor network that chooses discrete driving actions. In two simulated occluded scenarios, the authors report collision rates of about 2.1-3.1% for their method, versus 10.4-12.2% for independent reinforcement learning and 4.2-5.1% for cooperative early fusion. The significance is that collaborative navigation can be learned directly from experience, without expert demonstrations, and still respect real V2V bandwidth limits. If correct, this points toward cooperative driving policies that are both safer and more practical than sharing raw sensor data.

What carries the argument

The central mechanism is the compressed LiDAR feature message. Raw LiDAR points are preprocessed into ego-vehicle coordinates and passed through a two-layer convolutional network to produce a (4, 128, 128) feature tensor; this tensor is compressed for broadcast, decompressed by receiving vehicles, transformed into the receiver's perspective, and aggregated with the ego vehicle's own features. The aggregated tensor, together with metadata such as speed and dimensions, is the input to a MAPPO actor network, so the message strikes an intermediate-fusion balance: richer than object-level summaries, far smaller than raw point clouds. MAPPO supplies centralized training with decentralized execution, letting a common critic stabilize learning while each vehicle acts only on local observations plus received features.

What would settle it

Retrain the independent-RL and early-fusion baselines with the same number of episodes, random seeds, and reward coefficients as the collaborative method in the two occluded scenarios; if their collision rates fall to the 2-3% range, the claimed advantage is an artifact of training effort rather than of sharing compressed LiDAR features.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that intermediate-fusion collaborative control trained end-to-end with MAPPO yields safer policies in occluded scenarios than both independent learning and early fusion, while remaining bandwidth-feasible. In the occluded-intersection scenario the proposed method achieves a collision rate of 2.12% and success rate of 85.34%; in the blind-summit scenario, 3.07% and 94.14%. The relevant comparisons are independent RL at 10.43% and 12.15% collision and early fusion at 5.12% and 4.24% collision, with the ground-truth baseline at 4.87% and 1.91%. The authors also calibrate the communication cost: after 3D compression the shared feature messages use approximately 1.075 Mbps at 20 fps, within DSRC and C-V2X throughput, whereas transmitting the raw LiDAR point cloud would require roughly 80 Mbps.

Load-bearing premise

The load-bearing premise is that the independent-RL and early-fusion baselines were given the same training budget and random seeds as the proposed method, so the reported gap in collision and success rates reflects the architecture itself; the experiments section shows learning curves only for the proposed method and the ground-truth baseline, not for the comparison baselines.

Editorial extensions

If this is right

In the two tested scenarios the method reduces collision rates by about 70% on average relative to independent reinforcement learning, indicating that collaborative feature sharing is a viable safety mechanism for occluded intersections.
The compressed feature message uses roughly 1.075 Mbps at 20 fps, remaining below DSRC (2.0 Mbps) and C-V2X (7.2 Mbps) throughput, so the approach is deployable under current V2X bandwidth constraints.
The collaborative policy stays competitive with a ground-truth baseline that sees exact vehicle locations, suggesting the shared LiDAR features recover most of the value of full observability without requiring those locations to be known.
Performance tolerates up to roughly 20% LiDAR point dropout, indicating the approach is robust to realistic sensor degradation below that threshold.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the policy only receives features from agents within a 70-meter range, a natural follow-up is to vary the communication radius and measure the collision rate, predicting a sharp rise once occluded vehicles fall outside that radius.
The reported 20-30% dropout threshold suggests the learned policy depends on feature continuity; a future variant could make the transmission schedule itself learned, deciding when to broadcast, which would further reduce bandwidth but would need its own dropout robustness analysis.
The fully cooperative shared-reward setup means the method targets fleets of cooperative vehicles; applying it to mixed traffic with human-driven vehicles would require adding prediction of non-communicating agents or changing the reward structure.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a reasonable empirical paper, not a breakthrough. The novel bit is combining MAPPO with compressed intermediate LiDAR feature sharing for end-to-end cooperative control, trained from RL experience instead of behavior cloning, and they show a bandwidth calculation that puts them under DSRC/C-V2X limits. That combination is genuinely absent from the cited literature, and the dropout robustness experiment is a nice extra.

What it does well: the problem is well-motivated, the environment is described clearly, and the comparison against early fusion at least shows that compressed sharing isn't obviously worse than raw sharing. The learning curves for the proposed method look stable. The authors also honestly note that some collisions remain.

Soft spots: the central comparison against independent RL doesn't isolate the V2V feature-sharing mechanism. The independent RL baseline "tries to maximise an independent reward function," so it differs in reward and likely in centralized training, not just in message sharing. That conflation matters because the paper's stated contribution is the sharing, not cooperative credit assignment. The early fusion baseline partially addresses this, but the collision-rate differences between the proposed method and early fusion are small and the standard deviations overlap heavily (2.12±4.48 vs 5.12±4.44 in scenario 1; 3.07±3.15 vs 4.24±2.22 in scenario 2). No significance tests are reported, the table caption and text disagree on whether it's 100 or 1000 test runs, and there are no training curves or seed counts for the independent and early fusion baselines. Also, the CNN configuration (two conv layers) was selected after testing on these exact scenarios, so the reported numbers partly reflect tuning on the evaluation set. No code or environment release, which limits reproducibility but is not disqualifying.

The stress-test concern from your note is fair: the independent RL comparison conflates V2V sharing with reward changes. But the paper's headline is about the whole collaborative pipeline, so I wouldn't call the central claim false—just under-supported. The early fusion comparison gives some control for sharing, so the mechanism isn't completely unaddressed.

Bottom line: this paper is for people working on MARL for cooperative driving or V2X perception sharing. It deserves a serious referee—the novelty is real and the experiments are a reasonable first step—but the authors should be pushed to add significance testing, baseline training details, and ideally an ablation that isolates the feature-sharing channel from the cooperative reward.

Referee Report

4 major / 5 minor

Summary. The paper proposes an end-to-end collaborative control method for connected autonomous vehicles in occluded driving scenarios. The method extracts LiDAR features with a CNN, compresses them with Draco, shares them over a V2V network, and trains the resulting multi-agent policy with MAPPO. The authors introduce a CARLA-based occluded intersection gym environment and evaluate the method on two scenarios against rule-based, independent RL, early-fusion MAPPO, and ground-truth MAPPO baselines. They report lower collision rates and higher success rates for the proposed method than for independent RL and early fusion, along with a bandwidth analysis showing that the compressed features fit within DSRC/C-V2X limits. A robustness experiment examines performance under LiDAR point dropout.

Significance. If the reported results are reliable, the paper makes a useful contribution: it demonstrates that compressed intermediate LiDAR feature sharing can improve safety in occluded intersections within realistic bandwidth budgets, without relying on expert demonstrations. The proposed environment and pipeline are concrete and could serve as a testbed for future collaborative-driving work. However, the strength of the evidence is currently limited by overlapping standard deviations, the absence of significance tests, and a baseline design that conflates V2V feature sharing with changes in the learning objective. These issues are central to the paper's main claim, so the contribution is not yet established at the level required for publication.

major comments (4)

[Table I and Section V-A] The central claim of 'consistent superiority' over early fusion is not statistically supported. In the Occluded Intersection scenario, the proposed method's collision rate is 2.12 ± 4.48 versus 5.12 ± 4.44 for early fusion; the difference is well within one standard deviation. In the Blind Summit scenario, the corresponding rates are 3.07 ± 3.15 versus 4.24 ± 2.22, also overlapping. No significance tests, confidence intervals, or effect sizes are reported, and the number of training seeds used to produce these test statistics is not stated. The authors should report paired or unpaired significance tests across multiple seeds, confidence intervals, or at least the per-seed distributions. Without this, the abstract's claim of 'consistent superiority' overstates what the data show.
[Section V, Baseline description; Eq. (4)] The comparison against Independent RL does not isolate the effect of sharing compressed LiDAR features. The proposed method uses a fully cooperative shared reward (Eq. 4: 'agents share the same reward'), while the Independent RL baseline 'try to maximise an independent reward function.' Thus the lower collision rate could be due to cooperative credit assignment or MAPPO's centralized critic rather than the shared LiDAR features. The baseline description states that 'the MAPPO structure is the same for the multi-agent case, except for the message compression and sharing phase,' but it is ambiguous whether the independent baseline retains the shared reward and centralized critic. To support the causal claim, the authors should include an ablation that keeps all training components (shared reward, centralized critic, communication protocol) identical and varies only the presence or type of shared features, or at minimum report whether the independent baseline uses the same reward and critic structure.
[Section V-A and Figure 4] The baselines' training is not sufficiently documented. Figure 4 shows learning curves only for the proposed method and the ground-truth baseline; no training curves, episode counts, or seed counts are given for Independent RL or Early Fusion. As a result, it is impossible to verify that the baselines were trained to convergence with comparable compute budgets, and the performance gap might reflect unequal training effort rather than the method itself. The authors should report all learning curves, the number of seeds, and the training hyperparameters for every baseline, and ideally show that each baseline has converged.
[Section V-A and Table I caption] There is a direct inconsistency in the reported number of test runs: the text states 'the average and standard deviation of 100 independent testing runs,' while Table I's caption says the results are 'the average over 1000 independent test runs.' This discrepancy affects the interpretation of the reported standard deviations and must be corrected. The authors should also clarify whether the standard deviations are over episodes, over seeds, or over the mean across seeds, and report the corresponding quantities consistently.

minor comments (5)

[Abstract and Section IV-A] The word 'prepossesses' should be 'preprocesses' in both the abstract and the method description.
[Section II-B, reference [20]] The citation to 'Vedaldi et al.' for V2VNet is mis-attributed; the V2VNet paper is by Wang et al. and appears in the ECCV proceedings edited by Vedaldi et al. Please correct the citation.
[Table II] The environment parameter 'LiDAR points per second 16points/s' is implausibly low and likely a typo; please specify the intended point rate (e.g., number of points per scan or per second).
[Section V] There is a duplicated phrase: 'such as such as DSRC and C-V2X' should read 'such as DSRC and C-V2X.'
[Section IV-C, Step 2] The use of the Draco 3D compression library for (4,128,128) feature tensors is unusual, since Draco is designed for meshes; please clarify how it is applied to the feature map and how the '40 times' reduction factor is measured.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; the performance claims are measured empirical outcomes rather than reductions of predicted quantities to fitted inputs.

full rationale

This is an empirical paper whose central claims are measured experimental outcomes (collision rate, success rate, average speed in Table I) obtained from a custom CARLA-based simulator. The MAPPO objective in Eqs. (1)-(3) and the shared reward in Eq. (4) are inputs to the training procedure, and the reported metrics are test-set measurements, so no predicted quantity is derived by construction from a parameter fitted to those same quantities. The proposed method is compared against externally defined baselines (rule-based car following, independent RL, MAPPO with ground truth locations, and early fusion) rather than against a quantity that is equivalent to an input assumption of the method. The ambiguity about whether the Independent RL baseline retains the shared reward and centralized critic is a legitimate experimental-validity concern, not circularity, because it does not make the claimed superiority equivalent to its own definition. There is no load-bearing self-citation chain, no imported uniqueness theorem, and no ansatz smuggled in via citation; the cited external works provide algorithms, simulators, codecs, and standards, but the paper's safety/efficiency results are direct simulator measurements. Therefore no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No formal derivation; the central result is an empirical performance comparison. The paper depends on hand-tuned reward weights, the custom CARLA/OpenCDA environment faithfully representing occluded driving, lossless V2V message passing within range, and DRACO compression preserving decision-relevant information. None of these are validated with ablations or sensitivity analysis.

free parameters (5)

Reward weights (rcol, rdest, rspeed, rstep) = -5, +5, +1, -0.01
Set by hand in Equation 4 without sensitivity analysis; the safety-efficiency trade-off depends on them.
CNN architecture and feature size = two conv layers, output (4,128,128)
Chosen 'after testing various configurations' in Section IV-B; selection is based on the target scenarios and bandwidth, creating a risk of overfitting to these scenarios.
Communication range = 70 m
Set as 'realistic' in Appendix A, but no variation is tested to show robustness.
LiDAR configuration = 32 channels, 50 m range
Environment configuration; not varied across experiments.
MAPPO hyperparameters = not reported
Missing from the paper; reproducibility requires them.

assumptions (5)

domain assumption The CARLA/OpenCDA simulation with SUMO traffic accurately models real occluded intersection driving dynamics and sensor noise.
The central results are measured entirely inside this custom environment; no real-world or higher-fidelity validation.
domain assumption V2V communication delivers compressed features within 70 m without packet loss or meaningful latency.
Only LiDAR point dropout is tested; communication failures are not simulated.
domain assumption DRACO compression and decompression preserve enough information in the LiDAR features for the downstream policy to act safely.
Bandwidth savings are reported, but no reconstruction-quality or task-performance validation of the compressed features is provided.
domain assumption All agents optimize a fully cooperative shared reward, justifying the shared-reward MAPPO formulation.
Equation 4 assumes a common reward; in real traffic, vehicles may be self-interested.
ad hoc to paper The baselines are trained to convergence with comparable resources.
No training details are reported for Independent RL and Early Fusion; unequal training would invalidate the comparison.

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Cite this review

Pith. "Pith review of An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios." pith.science (2026). https://pith.science/paper/JAWG6DGC

@misc{pith2026241208562,
  author       = {Pith},
  title        = {Pith review of: An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JAWG6DGC}},
  note         = {Machine review of arXiv:2412.08562}
}

read the original abstract

Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to address this issue is through the use of Vehicle-to-Vehicle (V2V) networks that allow for the sharing of perception information with nearby agents, preventing catastrophic accidents. In this article, we propose a collaborative control method based on a V2V network for sharing compressed LiDAR features and employing Proximal Policy Optimisation to train safe and efficient navigation policies. Unlike previous approaches that rely on expert data (behaviour cloning), our proposed approach learns the multi-agent policies directly from experience in the occluded environment, while effectively meeting bandwidth limitations. The proposed method first prepossesses LiDAR point cloud data to obtain meaningful features through a convolutional neural network and then shares them with nearby CAVs to alert for potentially dangerous situations. To evaluate the proposed method, we developed an occluded intersection gym environment based on the CARLA autonomous driving simulator, allowing real-time data sharing among agents. Our experimental results demonstrate the consistent superiority of our collaborative control method over an independent reinforcement learning method and a cooperative early fusion method.

Figures

Figures reproduced from arXiv: 2412.08562 by the authors.

**Figure 2.** Collaborative MARL framework. LiDAR features are extracted from preprocessed point cloud data and shared within a V2V local communication [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Occluded scenarios. The first scenario corresponds to a blind intersection, where vehicles are occluded by tall trees. The second scenario corresponds [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Learning curves for MAPPO with ground truth locations of occluded [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Collision and Success rate as a function of LiDAR point dropout. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

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Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning

D. Kamran, C. F. Lopez, M. Lauer, and C. Stiller, “Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning,” Apr. 2020, arXiv:2004.04450 [cs]. [Online]. Available: http://arxiv.org/abs/2004.04450

work page Pith review arXiv 2020

[2] [2]

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning,

D. Isele, R. Rahimi, A. Cosgun, K. Subramanian, and K. Fujimura, “Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning,” Feb. 2018, arXiv:1705.01196 [cs]. [Online]. Available: http://arxiv.org/abs/1705.01196

arXiv 2018

[3] [3]

Broadcasting in V ANET,

O. Tonguz, N. Wisitpongphan, F. Bai, P. Mudalige, and V . Sadekar, “Broadcasting in V ANET,” in 2007 Mobile Networking for Vehicular Environments, May 2007, pp. 7–12

work page 2007

[4] [4]

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication

R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication,” June 2022, arXiv:2109.07644 [cs]. [Online]. Available: http://arxiv.org/abs/2109.07644

work page Pith review arXiv 2022

[5] [5]

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer,

R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer,” Aug. 2022, arXiv:2203.10638 [cs]. [Online]. Available: http://arxiv.org/abs/2203.10638

arXiv 2022

[6] [6]

COOPERNAUT: End- to-End Driving with Cooperative Perception for Networked Vehicles,

J. Cui, H. Qiu, D. Chen, P. Stone, and Y . Zhu, “COOPERNAUT: End- to-End Driving with Cooperative Perception for Networked Vehicles,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 17 231–17 241, arXiv:2205.02222 [cs]. [Online]. Available: http://arxiv.org/abs/2205.02222

arXiv 2022

[7] [7]

Exploring the Limitations of Behavior Cloning for Autonomous Driving,

F. Codevilla, E. Santana, A. M. L ´opez, and A. Gaidon, “Exploring the Limitations of Behavior Cloning for Autonomous Driving,” Apr. 2019, arXiv:1904.08980 [cs]. [Online]. Available: http://arxiv.org/abs/ 1904.08980

arXiv 2019

[8] [8]

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games,

C. Yu, A. Velu, E. Vinitsky, Y . Wang, A. Bayen, and Y . Wu, “The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games,” arXiv:2103.01955 [cs] , July 2021. [Online]. Available: http://arxiv.org/abs/2103.01955

arXiv 2021