arxiv: 2604.12418 · v1 · submitted 2026-04-14 · 💻 cs.RO · cs.AI

Recognition: unknown

RACF: A Resilient Autonomous Car Framework with Object Distance Correction

Chieh Tsai , Hossein Rastgoftar , Salim Hariri

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Pith reviewed 2026-05-10 15:43 UTC · model grok-4.3

classification 💻 cs.RO cs.AI

keywords autonomous vehiclesresilient perceptionobject distance correctiondepth cameraLiDARsensor redundancyreal-time systemssafety-critical driving

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The pith

A resilient framework corrects depth camera distance errors in autonomous cars by switching to LiDAR and kinematics data when inconsistencies appear.

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

The paper presents RACF, a framework that adds a correction layer to autonomous vehicle perception to handle cases where the depth camera produces faulty obstacle distances due to corruption or attacks. It monitors outputs across the depth camera, LiDAR, and vehicle kinematics; when the camera readings diverge, a gate activates the Object Distance Correction Algorithm to adjust them using the other sources. Experiments on a physical testbed showed up to 35 percent lower root-mean-square error under strong corruption, along with better stopping behavior and faster braking responses, all while running in real time. A sympathetic reader would care because unreliable distance estimates directly affect collision avoidance and safe stopping distances in safety-critical driving.

Core claim

The authors establish that their RACF framework, incorporating the Object Distance Correction Algorithm, detects inconsistencies in depth camera estimates via a cross-sensor gate and corrects them using LiDAR range data combined with physics-based vehicle kinematics, yielding measurable gains in estimation accuracy and control safety on a Quanser QCar 2 platform.

What carries the argument

The cross-sensor gate and Object Distance Correction Algorithm (ODCA), which activates LiDAR-plus-kinematics correction precisely when depth camera distance outputs are inconsistent with the other sources.

If this is right

Distance estimation RMSE drops by up to 35 percent when the depth camera is heavily corrupted.
Stop compliance improves because corrected distances allow earlier and more accurate braking commands.
Braking latency decreases as the framework supplies timely, consistent obstacle information.
The entire correction process runs fast enough for real-time operation on embedded hardware.
The approach remains lightweight because it adds only a conditional correction step rather than full sensor fusion at every timestep.

Where Pith is reading between the lines

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

The same gate-and-correct pattern could be applied to other perception outputs such as object classification or lane detection.
In field deployments the framework might lower the success rate of camera-targeted adversarial attacks by falling back to harder-to-spoof sensors.
Integration into existing autonomous stacks would require only modest additional compute since LiDAR and odometry are already present in most vehicles.
Longer-term testing on public roads could show whether kinematics assumptions hold under slippery surfaces or strong wind.

Load-bearing premise

The system can reliably detect when the depth camera is wrong and that LiDAR plus kinematics will stay accurate enough to supply the correct distance value.

What would settle it

A controlled test run in which the depth camera is corrupted yet the gate does not trigger correction, resulting in RMSE values that remain as high as the uncorrected baseline.

Figures

Figures reproduced from arXiv: 2604.12418 by Chieh Tsai, Hossein Rastgoftar, Salim Hariri.

**Figure 2.** Figure 2: Resilient autonomous car framework. Redundancy is achieved via depth and LiDAR sensing, while diversity is provided by a physics/kinematic distance evolution branch. The distance fusion module performs trust & consistency checks to produce a robust distance estimate for car brake and control functions. the Quanser QCar 2 platform and evaluated its performance under weak, mid, and strong corruption attack r… view at source ↗

**Figure 3.** Figure 3: Perception-layer realization of ODCA within RACF. ChronosV2 provides uncertainty-aware temporal features. A lightweight delta head predicts additive depth corrections. A LiDAR-based consistency gate controls fusion, producing a conservative control-ready distance output. depth and LiDAR distance evidence, and (ii) diversity from short-horizon physical consistency (kinematics). Rather than unconditional fus… view at source ↗

**Figure 5.** Figure 5: Strong-strength attack comparison. Left: Chronos [4]. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 4.** Figure 4: Attack scenarios used in evaluation. (a) Controlled offline corruption applied to depth/confidence signals. (b) Physical adversarial patch deployed during live operation, suppressing stop-sign detection and inducing distance dropout. C. Hybrid Attack Evaluation We evaluate resilience under both offline corruption and closed-loop physical patch attacks. Offline attacks introduce depth bias, confidence degra… view at source ↗

**Figure 6.** Figure 6: Resilience summary across attack strengths (higher is better). Radar axes aggregate RMSE-based robustness at weak/mid/strong, bounded degradation (BD), and average gain vs. Chronos (RGR). TABLE III: Comparison with representative sensor-level baselines under strong attacks. Method RMSE ↓ LiDAR measurement 0.600 EKF fusion [19] 0.450 Chronos forecast [4] 0.503 ODCA (Ours) 0.323 [PITH_FULL_IMAGE:figures/fu… view at source ↗

**Figure 7.** Figure 7: Attack-related diagnostic quality (↑ better). Heatmap visualization of AUROC and AUPRC across weak, mid, and strong attack regimes. Cross-sensor residual (XS) and correction magnitude (Chg) are reported for each method. Exact numerical values are overlaid for clarity [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: Model complexity vs. repair accuracy (Strong). [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

**Figure 9.** Figure 9: High-risk stop-sign scenarios on the indoor map. The ego vehicle (blue arrow) encounters cross-traffic from other vehicles (red arrow) at conflict-prone intersections. The stop sign is placed in these regions to evaluate braking safety under attack. E. Analysis and Discussion ODCA achieves the lowest RMSE and MAE across all attack levels (Table II), demonstrating robust distance recovery under increasing c… view at source ↗

read the original abstract

Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss and/or severe physical damages. Reliable real-time perception is therefore critically important for their safe operations and acceptability. For example, vision-based distance estimation is vulnerable to environmental degradation and adversarial perturbations, and existing defenses are often reactive and too slow to promptly mitigate their impacts on safe operations. We present a Resilient Autonomous Car Framework (RACF) that incorporates an Object Distance Correction Algorithm (ODCA) to improve perception-layer robustness through redundancy and diversity across a depth camera, LiDAR, and physics-based kinematics. Within this framework, when obstacle distance estimation produced by depth camera is inconsistent, a cross-sensor gate activates the correction algorithm to fix the detected inconsistency. We have experiment with the proposed resilient car framework and evaluate its performance on a testbed implemented using the Quanser QCar 2 platform. The presented framework achieved up to 35% RMSE reduction under strong corruption and improves stop compliance and braking latency, while operating in real time. These results demonstrate a practical and lightweight approach to resilient perception for safety-critical autonomous driving

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

RACF gives a concrete cross-sensor gate plus ODCA correction for depth-camera distance errors, with real-time testbed results, but the resilience story depends on an untested assumption that LiDAR and kinematics stay clean.

read the letter

The paper's main contribution is a lightweight framework that watches for inconsistency between a depth camera's obstacle distance and the readings from LiDAR plus simple vehicle kinematics, then applies a correction step when the gate triggers. They implemented this on the Quanser QCar 2 and report up to 35% RMSE drop under their corruption tests along with faster, more reliable stopping.

Referee Report

2 major / 2 minor

Summary. The paper introduces the Resilient Autonomous Car Framework (RACF) incorporating an Object Distance Correction Algorithm (ODCA). It uses redundancy across a depth camera, LiDAR, and physics-based kinematics, with a cross-sensor gate that detects inconsistencies in depth-camera distance estimates and activates correction. Experiments on a Quanser QCar 2 testbed report up to 35% RMSE reduction under strong corruption, along with improved stop compliance and braking latency, while maintaining real-time operation.

Significance. If validated, the work offers a lightweight, practical method for enhancing perception robustness in safety-critical autonomous vehicles by exploiting sensor diversity without heavy computation. The real-time testbed results and focus on stop compliance provide a concrete step toward deployable resilience, though generalizability remains to be established.

major comments (2)

[Experimental evaluation (inferred from abstract and framework description)] The central quantitative claim of up to 35% RMSE reduction (and qualitative improvements in stop compliance and braking latency) is presented without details on experimental design, including how corruption was applied to the depth camera, choice of baselines, number of trials, statistical significance, or error bars. This information is required to evaluate whether the reported gains support the resilience claim.
[Framework description and ODCA section] The cross-sensor gate and ODCA correction rely on the assumption that LiDAR and kinematics remain reliable when the depth camera is corrupted. No ablation studies or attack models address correlated degradations (e.g., fog or spoofing affecting multiple modalities simultaneously), which directly undermines the general resilience property asserted in the abstract.

minor comments (2)

[Abstract and §3 (framework)] The abstract and framework overview would benefit from explicit definitions of the cross-sensor gate threshold and the exact correction formula in ODCA to improve reproducibility.
[Experimental section] Clarify the testbed setup, including sensor specifications on the Quanser QCar 2 and the precise metrics for 'stop compliance' and 'braking latency'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which have helped us improve the clarity and rigor of the manuscript. We address each major comment point by point below and have revised the paper accordingly.

read point-by-point responses

Referee: The central quantitative claim of up to 35% RMSE reduction (and qualitative improvements in stop compliance and braking latency) is presented without details on experimental design, including how corruption was applied to the depth camera, choice of baselines, number of trials, statistical significance, or error bars. This information is required to evaluate whether the reported gains support the resilience claim.

Authors: We agree that the original manuscript lacked sufficient detail on the experimental protocol. In the revised version, we have substantially expanded the 'Experimental Evaluation' section to specify: (1) the corruption model applied to the depth camera (additive Gaussian noise with variance levels calibrated to produce 'strong corruption' as defined in the abstract); (2) the baselines (raw depth-camera estimates and a naive sensor-fusion average); (3) the number of trials (20 independent runs per scenario on the Quanser QCar 2); (4) statistical analysis (paired t-tests with reported p-values confirming significance of the RMSE reduction); and (5) error bars (standard deviation) on all quantitative plots. These additions directly support the 35% RMSE claim and the observed improvements in stop compliance and braking latency. revision: yes
Referee: The cross-sensor gate and ODCA correction rely on the assumption that LiDAR and kinematics remain reliable when the depth camera is corrupted. No ablation studies or attack models address correlated degradations (e.g., fog or spoofing affecting multiple modalities simultaneously), which directly undermines the general resilience property asserted in the abstract.

Authors: The referee correctly identifies a key modeling assumption. Our framework targets modality-specific degradations (e.g., visual corruption that affects only the depth camera), which is justified by the physical properties of the sensors: LiDAR operates on time-of-flight rather than image intensity and kinematics derive from vehicle dynamics. We have added an explicit 'Assumptions and Limitations' subsection that states this scope, explains the rationale, and acknowledges that simultaneous multi-modal attacks (fog, spoofing) are outside the current evaluation. No new ablation experiments on correlated attacks are included, as they would require additional testbed runs beyond the scope of a revision; instead, we have strengthened the abstract and conclusion to qualify the resilience claim as applying to independent sensor failures and flagged correlated degradations as future work. revision: partial

Circularity Check

0 steps flagged

No circularity detected in RACF derivation or claims

full rationale

The paper describes an empirical framework that activates ODCA correction upon detecting inconsistency via cross-sensor gate comparing depth camera output against LiDAR and physics-based kinematics. No equations, fitted parameters, or self-citations are presented that define a result in terms of itself or rename a fitted quantity as a prediction. The 35% RMSE reduction and latency improvements are reported from independent Quanser QCar 2 experiments under controlled single-sensor corruption, not derived tautologically from the framework definition. The approach is self-contained against external benchmarks and does not invoke uniqueness theorems or prior author work to force its structure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the framework builds on standard multi-sensor redundancy without detailing any ad-hoc assumptions or new postulated components.

pith-pipeline@v0.9.0 · 5504 in / 1196 out tokens · 58678 ms · 2026-05-10T15:43:40.389192+00:00 · methodology

discussion (0)

Reference graph

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