Secure Data Fusion via Redundant V2V Channels

• The paper introduces a secure data fusion framework that leverages redundant V2V channels to achieve robust state estimation even under adversarial attacks.
• It employs a trimmed mean and subset consistency approach to fuse multi-modal sensor data and isolate malicious nodes effectively.
• Experimental results demonstrate significant reductions in error metrics like GOSPA, validating the method's practical resilience and efficiency.

Secure data fusion via redundant vehicle-to-vehicle (V2V) channels is foundational to the resilience and trustworthiness of cooperative perception and state estimation in connected and automated vehicles (CAVs). By leveraging the inherent redundancy from multiple independent V2V communication pathways, modern fusion frameworks achieve robust state estimation and isolation of malicious (or faulty) nodes, even in the presence of severe adversarial activity or sensor limitations. This paradigm underpins both secure estimation—the process of synthesizing consistent, attack-resilient state information—and secure track management, which is crucial for advanced driver-assistance systems (ADAS) and autonomous mobility.

1. System and Threat Models

At the system level, CAVs exist in a dynamic set $S(k)$ of $N$ vehicles at discrete time steps $k$. Each vehicle $i$ is described by a (possibly vector-valued) state $x_i(k)$ encoding critical kinematics such as position and velocity. The minimal discrete-time vehicle dynamics are

$x_i(k+1) = A\,x_i(k) + w_i(k)$

where $A$ is a known state transition matrix and $w_i(k)$ represents unknown but bounded disturbances.

Each CAV is equipped with local sensors (radar, camera, navigation) which provide measurements of its own state and, when applicable, relative states of nearby vehicles $j \in \mathcal{N}_i(k)$. The collected measurements, such as self-observations $y_{i|i}(k)$ and relative observations $y_{i|j}(k)$, are reported to an aggregation engine, often via cloud-based or edge-based architecture using V2V or V2X channels (Yang et al., 2021).

The threat model admits the possibility that up to $f_i(k)$ of the $N_i(k)$ available reporting channels for vehicle $i$ may be permanently or transiently compromised. Malicious vehicles can inject arbitrary 'attack' signals $a_i(k)$ into their own reporting channels, subject only to the sparse attack constraint:

$|\mathrm{supp}\, a_i(k)| \leq f_i(k) < N_i(k)/2$

ensuring that the number of benign (attack-free) reports always exceeds the number of adversarial ones.

2. Redundant V2V Channel Architecture

Redundancy in sensor fusion is achieved by combining not only the self-reported state of each vehicle, but also the indirect observations supplied by vehicle neighbors, i.e., each CAV's state is reported both directly and through relative position reports of adjacent vehicles. Thus, the aggregate measurement vector for vehicle $i$, denoted $Y_i(k)$, can be written as

$Y_i(k) = H_i\,x_i(k) + m_i(k) + a_i(k)$

with $$H_i = [1;\ldots;1] \in \mathbb{R}^{N_i(k) \times 1}$$, additive bounded measurement noise $m_i(k)$, and attack vector $a_i(k)$. For each state component, $N_i(k)$ independent channels deliver redundant data at every $k$.

In practical implementations (Billington et al., 2024), the V2V input is further augmented with local sensor tracks (e.g., radar, camera, vision-detected bounding boxes), all transformed to a common Cartesian frame via coordinate transformations: $$\begin{align*} X &= (N(\phi)+h)\cos\phi\cos\lambda \ Y &= (N(\phi)+h)\cos\phi\sin\lambda \ Z &= \left(\frac{b^2}{a^2}N(\phi)+h\right)\sin\phi \end{align*}$$ followed by local offset adjustment.

The key architectural feature is the presence of both direct V2V (DSRC and C-V2X) and multi-modal sensor information, enabling multiple, independently acquired perspectives on any tracked object.

3. Secure Fusion Algorithms Exploiting Redundancy

The core fusion mechanism leverages subset consistency to mitigate the influence of compromised reports. For each vehicle $i$, and for each scalar component, one considers all possible subsets $J \subset \{1,\ldots,N_i(k)\}$ of size $N_i(k)-f_i(k)$. For each such subset:

• Compute the sample mean:

$$\hat{x}_J(k) = \frac{1}{|J|} \sum_{j \in J} Y_{ij}(k)$$

• Compute the maximal inconsistency within the subset:

$$\pi_J(k) = \max_{j \in J} |Y_{ij}(k) - \hat{x}_J(k)|$$

The estimator then selects the subset $\sigma(k)$ with the minimal worst-case deviation,

$\sigma(k) = \arg\min_{J:|J|=N_i-f_i} \pi_J(k)$

and declares the estimate

$\hat{x}_i(k) = \hat{x}_{\sigma(k)}(k)$

This procedure is equivalent to a “trimmed mean” on the measurement stack, where the configuration is such that strictly less than half of the measurements are corrupted. Under the assumption $f_i(k) < N_i(k)/2$, the estimation error satisfies [(Yang et al., 2021), Theorem 2]:

$$|e_i(k)| = |\hat{x}_i(k) - x_i(k)| \leq 3\,\|m\|_\infty$$

for all $k$, with no requirement for statistical assumptions about the noise distribution, only boundedness.

For multi-sensor, multi-track fusion (e.g., “priority track lists”), Mahalanobis-distance-based gating, fusion weight adaptation, and sequential confirmation/deletion logic are utilized. Association of radar/camera tracks $s$ and V2V tracks $v$ is declared only if

$$D_M = (z_v-z_s)^T C^{-1}(z_v - z_s) \leq \chi^2_{d,\alpha}$$

with fusion weights

$$w_{\text{sensor}} = \frac{1/\sigma^2_{\text{sensor}}}{1/\sigma^2_{\text{sensor}} + 1/\sigma^2_{\text{V2V}}} \qquad w_{\text{V2V}} = \frac{1/\sigma^2_{\text{V2V}}}{1/\sigma^2_{\text{sensor}} + 1/\sigma^2_{\text{V2V}}}$$

(Billington et al., 2024).

4. Attack Isolation and Security Mechanisms

Attack isolation builds on the secure estimation step. Once state estimates $\hat{x}_i(k)$ are available, reported measurements $Y_{ji}(k)$ from vehicle $i$ about its neighbor $j$ are checked for consistency:

$|\hat{x}_j(k) - Y_{ji}(k)| \leq 4\,\|m\|_\infty$

for all $j \in \mathcal{N}_i(k) \cup \{i\}$. If this constraint is violated for any $j$, the vehicle $i$ is flagged as malicious (Yang et al., 2021).

Further security protocols include:

• Initial Vehicle Identification: No Basic Safety Message (BSM) is accepted until corroborated by matching camera/radar detection within a gating region.
• Cross-Channel Redundancy: BSMs are admitted only if two or more independent on-board units (OBUs) agree on reported state and time.
• Cryptographic Verification: Use of IEEE 1609.2 signatures to detect message tampering; quarantining of unverified or disputed messages.
• Anomaly Detection: BSMs inconsistent with EKF predictions or displaying improbable motion profiles are temporarily quarantined.

Redundancy across independent V2V technologies (DSRC, C-V2X) further mitigates single-channel spoofing attacks, as spatial or temporal inconsistency across these paths leads to isolation or rejection of the corresponding track (Billington et al., 2024).

5. Complexity, Scalability, and Performance

The outlined estimation procedure, in its naively exhaustive form, scales combinatorially with the number of redundant channels $N_i(k)$. However, as operational $f_i(k)$ is typically small and the communication neighborhood bounded (by V2V range constraints), practical implementations use partial sorting or statistical trimming ($O(N_i \log N_i)$) to achieve acceptable runtime (Yang et al., 2021).

Communication overhead consists of each vehicle transmitting both its own state and relative positions of neighbors, amounting to $O(|\mathcal{N}_i(k)|)$ packets per vehicle per time-step (up to $O(N^2)$ in high-density graphs). Scalability is therefore closely tied to the underlying V2V/V2X networking topology.

Empirical validation in MATLAB/Simulink and road-intersection simulation environments demonstrates that redundant V2V fusion significantly reduces the Generalized Optimal SubPattern Assignment (GOSPA) metric compared to local-only fusion; for instance, mean GOSPA drops from 56.12 (sensor only) to 7.52 (V2V only), with “priority fusion” (sensor+V2V) yielding robust compromise at 48.62 (Billington et al., 2024). Errors due to missed or falsely tracked objects are likewise reduced, and all simulated attack scenarios see exact detection and isolation when fewer than half the channels are compromised (Yang et al., 2021).

Configuration	Mean GOSPA	Missed Target	False Track
Local Fusion (sensor)	56.12	30.0	47.43
V2V Only	7.52	0.0	0.0
Priority Fusion (sensor+V2V)	48.62	21.2	42.43

6. Representative Scenarios and Limitations

Simulation studies illustrate the impact of this secure redundant fusion:

• Scalar stacking: In a small vehicle set $(N=5)$, even with two vehicles injecting large Gaussian falsifications per round and nontrivial noise, estimation errors stay below 0.05 for the robust estimator, outperforming reference techniques by a wide margin (Yang et al., 2021).
• Highway multi-lane scenario: With vehicles frequently entering/exiting communication range and two persistent attackers (vehicles 4 and 5), cloud-based estimation remained within the calculated error bounds ($3\gamma$), and all attacks were immediately isolated upon detection.

Despite these guarantees, adversarial resilience is predicated on the key assumption that $f_i(k)<N_i(k)/2$ for every tracked vehicle and time instant. If this threshold is exceeded, no deterministic estimator can disambiguate state from coordinated attack.

Adaptive thresholding, flexible deletion/confirmation windows, and cross-modal fusion (incorporating both vision and V2V data) permit continued robust operation even in scenarios with high occlusion rates or intermittent sensor dropouts, though at the cost of higher communication and computation (Billington et al., 2024).

7. Implications and Research Directions

Secure data fusion via redundant V2V channels unifies principles of distributed estimation, adversarial signal processing, and multi-modal data association. The demonstrated efficiency and guarantees position these frameworks as central tools for CAVs operating in semi-trusted environments.

Future directions may include resource-adaptive channel selection to optimize redundancy under bandwidth and latency constraints, deeper integration with physical-layer authentication, and generalization to systems with dynamically varying or unknown neighborhood structures. A plausible implication is the potential for more widespread deployment of these techniques given ongoing advances in V2X networking infrastructure and vehicular cyber-physical system security.

For comprehensive technical elaboration, algorithmic proofs, and system-theoretic analyses, see (Yang et al., 2021) and (Billington et al., 2024).