Cooperative Intelligence for Heavy Commercial Vehicles: An Edge-Cloud V2X Architecture for Predictive Risk Mitigation

Overview

Heavy commercial vehicles (HCVs) present disproportionate safety risk due to mass, braking distance, blind spots, maneuverability constraints, and extended duty cycles. These characteristics increase the impact of delayed hazard perception and delayed mitigation.

Why HCV Safety Needs a New Approach

Existing approaches can be effective but often incomplete when isolated:

• ▸Local-only ADAS is constrained by sensing horizon and line-of-sight
• ▸Cloud-only telematics provides fleet-level oversight but may be too delayed for sub-second intervention
• ▸Corridor-level hazards can emerge beyond a single vehicle's observation boundary

Research Contribution

This is a comparative architecture research paper. The key contribution is a proposed three-layer cooperative architecture that combines vehicle edge intelligence, V2X cooperative hazard exchange, and cloud-based fleet-level prediction and policy feedback.

Proposed Architecture

1) Vehicle Edge Layer

• ▸Local sensor fusion
• ▸Real-time risk scoring
• ▸Immediate mitigation actions

2) V2X Communication Layer

• ▸V2V, V2I, and V2N hazard exchange
• ▸Upstream hazard dissemination
• ▸Corridor context propagation

3) Cloud Intelligence Layer

• ▸Fleet-level forecasting
• ▸Policy and model refinement
• ▸Cross-vehicle trend learning

Results

These results are simulation-based and should be interpreted as architectural feasibility, not deployment proof.

Conclusion

This work presents a practical architecture direction for connected, predictive, and cooperative HCV safety systems. It demonstrates representative architectural feasibility and motivates future real-world validation across operational fleets.