arxiv: 2605.04355 · v1 · submitted 2026-05-05 · 💻 cs.CV

Recognition: unknown

InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making

Mustafa Sakhaia , Kaung Sithua , Min Khant Soe Okea , Maciej Wielgosza

Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:57 UTC · model grok-4.3

classification 💻 cs.CV

keywords autonomous drivingsensor fusionevent camerasDVStransformerreinforcement learningCARLAmultimodal perception

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The pith

Adding event cameras to InterFuser fusion raises route completion to 100 percent on CARLA benchmarks.

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

The paper extends the InterFuser architecture by adding Dynamic Vision Sensors as a third input alongside RGB cameras and LiDAR. It introduces a token-based fusion method that feeds accumulated event frames directly into the transformer backbone. The goal is to overcome motion blur and latency problems that plague standard sensors in high-speed or high-contrast driving scenes. A sympathetic reader would care because more reliable multimodal perception could translate into fewer failures when lighting changes rapidly or vehicles move quickly. Evaluation on CARLA Leaderboard tasks shows the extended model reaches a Driving Score of 77.2 while completing routes at 100 percent.

Core claim

The authors claim that integrating DVS data through a novel token-based fusion strategy into the transformer backbone of InterFuser produces a more robust driving policy. This policy leverages the complementary strengths of RGB, LiDAR, and asynchronous event streams to maintain performance where conventional sensors degrade. On the CARLA Leaderboard the resulting agent records a competitive Driving Score of 77.2 and perfect Route Completion of 100 percent, indicating that event-based vision supplies useful information precisely in adverse lighting and dynamic conditions.

What carries the argument

Token-based fusion strategy that converts accumulated event frames into tokens and merges them with RGB and LiDAR tokens inside the transformer backbone of the extended InterFuser model.

If this is right

The agent maintains higher reliability when traditional cameras suffer motion blur at high speeds.
Perception stays effective in scenes with extreme brightness changes where RGB saturates or loses detail.
The same fusion approach can be applied to other reinforcement-learning driving policies that already use transformer backbones.
Event data reduces dependence on perfect synchronization between RGB and LiDAR streams.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method may generalize to real-vehicle hardware if event cameras are mounted alongside existing sensors.
Further gains could appear if the fusion tokens are weighted dynamically according to scene conditions.
Similar token fusion could be tested in other domains that need low-latency vision such as drone navigation.
Longer evaluation runs on CARLA might reveal whether the 100 percent route completion holds across more weather and traffic variations.

Load-bearing premise

The token-based fusion method actually succeeds in letting the transformer use event information in a way that measurably improves robustness over RGB-plus-LiDAR alone.

What would settle it

Re-running the same CARLA evaluation with the DVS branch removed and observing that the Driving Score and Route Completion stay statistically unchanged or improve.

Figures

Figures reproduced from arXiv: 2605.04355 by Kaung Sithua, Maciej Wielgosza, Min Khant Soe Okea, Mustafa Sakhaia.

**Figure 1.** Figure 1: Overview of the InterFuserDVS Architecture. The model fuses RGB images, LiDAR point clouds, and DVS event frames. Specific backbones (ResNet for RGB/DVS, PointNet for LiDAR) extract features which are tokenized and processed by a global Transformer Encoder. The Decoder predicts waypoints, traffic light states, and occupancy maps, which are used by the Safety Controller to generate final control commands. T… view at source ↗

**Figure 2.** Figure 2: InterFuserDVS Control Loop: High-level overview of the sensor fusion, transformer processing, and control generation pipeline. Note that the Safety Validation block takes the PID command as input and can override it. where 𝑤𝑝 is the 𝓁1 loss between predicted and ground-truth waypoints. 𝑡𝑟𝑎𝑓 𝑓 𝑖𝑐 is a specialized Multi-View Traffic Loss (MVTL) that enforces consistency between the predicted object density… view at source ↗

**Figure 3.** Figure 3: Safety Validation Logic: The hybrid controller logic that overrides PID proposals based on traffic light states and predicted future occupancy. It specifically checks if the vehicle is close to a junction before enforcing red light stops, preventing deadlocks. • Driving Score (DS): The primary metric, calculated as the product of Route Completion and (1 - Infraction Penalty). • Route Completion (RC): The p… view at source ↗

**Figure 4.** Figure 4: Future Architecture Design: Integrating Spiking Neural Networks (SNN) and Asynchronous Fusion Transformer (AFT). The pipeline preserves the asynchronous nature of DVS events through the SNN backbone, using a Discretizer to align spike features with synchronous RGB and LiDAR data before fusion. Sakhai et al.: Preprint submitted to Elsevier Page 14 of 17 view at source ↗

read the original abstract

Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often struggle in high-dynamic- range scenes or high-speed scenarios due to motion blur and latency. Dynamic Vision Sensors (DVS), or event cameras, offer a paradigm shift by capturing asynchronous brightness changes with microsecond temporal resolution and high dynamic range. In this paper, we propose an extended architecture of the state-of-the-art InterFuser model, integrating DVS as an additional modality to enhance perception reliability. We introduce a novel token-based fusion strategy that incorporates accumulated event frames into the transformer-based backbone of InterFuser. Our method leverages the complementary nature of RGB, LiDAR, and DVS data. We evaluate our approach on the Car Learning to Act (CARLA) Leaderboard benchmarks, demonstrating that the inclusion of DVS improves the robustness of the driving agent, achieving a competitive Driving Score of 77.2 and a superior Route Completion of 100%. The results indicate that event-based vision is a promising direction for improving safety and performance in adverse lighting and dynamic conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Extends InterFuser with token-based DVS fusion and hits 77.2 DS on CARLA, but no ablation isolates what the events actually add.

read the letter

The main takeaway is that the authors bolt event camera input onto the InterFuser transformer via a token fusion step and report a 77.2 driving score plus 100% route completion on the CARLA leaderboard. That puts the combined system in a reasonable range, but the evidence does not yet show the DVS is responsible for any robustness gain. What is new is the specific token-based way they accumulate event frames and feed them as an extra modality alongside RGB and LiDAR. It is a direct, architecture-preserving extension rather than a new backbone or training trick. The paper does well by keeping the original InterFuser structure intact and demonstrating that the addition runs end-to-end on the benchmark without obvious breakage. The results are at least on the public leaderboard, so the numbers can be checked by others. The soft spots are straightforward. There is no ablation that removes only the DVS branch or compares against a matched run of the original InterFuser under identical training conditions. RL driving agents are sensitive to seeds, hyperparameters, and data order, so a single score cannot separate the contribution of the event data from other changes. The abstract also gives no error bars, no multiple runs, and no details on event accumulation, tokenization, or fusion implementation. These gaps make it hard to judge whether the claimed complementarity in high-dynamic-range or high-speed cases actually holds. This paper is for people already working with InterFuser or similar transformer fusion setups who want a quick way to test event cameras in driving. A reader outside that niche gets little reusable method or data. It deserves peer review because the idea is incremental but reasonable and the leaderboard numbers are competitive; the authors can fix the controls and details in revision rather than being rejected outright.

Referee Report

3 major / 1 minor

Summary. The paper proposes InterFuserDVS, an extension of the InterFuser architecture that adds Dynamic Vision Sensors (DVS) as a third modality. It introduces a token-based fusion mechanism to incorporate accumulated event frames into the transformer backbone alongside RGB and LiDAR inputs. The central empirical claim is that this fusion improves robustness in high-dynamic-range and high-speed driving scenarios, as evidenced by a Driving Score of 77.2 and 100% Route Completion on the CARLA Leaderboard.

Significance. If the reported gains can be isolated to the DVS modality through controlled experiments, the work would provide concrete evidence that event-based sensing complements conventional cameras and LiDAR in RL-based autonomous driving, particularly under motion blur and lighting extremes. The absence of such controls currently prevents any assessment of whether the result advances the state of multi-modal fusion.

major comments (3)

[Abstract] Abstract: The reported Driving Score of 77.2 and Route Completion of 100% are presented without any matched baseline run of the original InterFuser or an ablation that removes only the DVS branch while holding architecture, RL training procedure, and CARLA scenario distribution fixed. Given the high variance typical of RL driving agents, a single scalar score does not establish that the token-based fusion produces measurable robustness gains.
[Methods] Methods / fusion description: The token-based fusion strategy is introduced at a conceptual level but supplies no equations, pseudocode, or architectural diagram showing how accumulated event frames are tokenized, embedded, and cross-attended with RGB and LiDAR tokens inside the transformer backbone. Without these details the claim that DVS data are “effectively incorporated” cannot be evaluated.
[Experiments] Experiments: No implementation details, training hyperparameters, random seeds, error bars, statistical tests, or leaderboard comparison table are provided. The manuscript therefore offers no way to determine whether the 77.2 score differs from prior InterFuser results by more than training stochasticity.

minor comments (1)

[Abstract] Abstract: The phrase “competitive Driving Score” is used without stating the current leaderboard rank or the exact metric definitions employed by CARLA.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive review. We agree that the current manuscript lacks the necessary controls, technical details, and experimental rigor to substantiate the claimed benefits of DVS integration. We will perform a major revision to address all points raised.

read point-by-point responses

Referee: [Abstract] Abstract: The reported Driving Score of 77.2 and Route Completion of 100% are presented without any matched baseline run of the original InterFuser or an ablation that removes only the DVS branch while holding architecture, RL training procedure, and CARLA scenario distribution fixed. Given the high variance typical of RL driving agents, a single scalar score does not establish that the token-based fusion produces measurable robustness gains.

Authors: We acknowledge the validity of this criticism. The revised manuscript will include a matched baseline evaluation of the original InterFuser under identical training conditions, random seeds, and CARLA scenario distributions. We will also add an ablation study that isolates the DVS branch by removing it while keeping all other components fixed. These additions will enable direct quantification of the contribution of the token-based fusion beyond training stochasticity. revision: yes
Referee: [Methods] Methods / fusion description: The token-based fusion strategy is introduced at a conceptual level but supplies no equations, pseudocode, or architectural diagram showing how accumulated event frames are tokenized, embedded, and cross-attended with RGB and LiDAR tokens inside the transformer backbone. Without these details the claim that DVS data are “effectively incorporated” cannot be evaluated.

Authors: We agree that the fusion mechanism requires precise specification. In the revision we will add the full mathematical formulation for event-frame accumulation, tokenization, embedding, and the cross-attention equations within the transformer. We will also include pseudocode for the fusion module and a detailed architectural diagram that illustrates the data flow from DVS events through to the shared backbone. revision: yes
Referee: [Experiments] Experiments: No implementation details, training hyperparameters, random seeds, error bars, statistical tests, or leaderboard comparison table are provided. The manuscript therefore offers no way to determine whether the 77.2 score differs from prior InterFuser results by more than training stochasticity.

Authors: We accept this observation. The revised experiments section will report complete implementation details, the full set of training hyperparameters, the number of random seeds, standard deviations across runs, results of statistical significance tests, and a comparison table that places the 77.2 score against the original InterFuser and other published CARLA leaderboard entries under consistent evaluation protocols. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical simulation results with no derivations or self-referential reductions.

full rationale

The manuscript describes an architectural extension of InterFuser that adds a DVS branch and a token-based fusion module, then reports CARLA leaderboard metrics (Driving Score 77.2, Route Completion 100%). No equations, parameter-fitting procedures, or derivation steps appear in the provided text. The central claim rests on direct simulation outcomes rather than any quantity that is defined in terms of itself or obtained by renaming a fitted input. Self-citations, if present, are not load-bearing for any mathematical result. The evaluation chain is therefore self-contained against the external CARLA benchmark and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard domain assumptions in multi-modal sensor fusion for driving; no free parameters, new axioms beyond common practice, or invented entities are introduced in the abstract.

axioms (1)

domain assumption RGB, LiDAR, and DVS modalities are complementary for perception in adverse conditions
Invoked when stating that the method leverages their complementary nature.

pith-pipeline@v0.9.0 · 5511 in / 1175 out tokens · 88020 ms · 2026-05-08T16:57:19.438760+00:00 · methodology

discussion (0)

Reference graph

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