Across national road networks, cargo and commercial vehicles form the backbone of modern logistics. Yet behind this lifeline lies a persistent safety risk. According to the Korean National Police Agency, while overall traffic fatalities have been declining, accidents involving commercial vehicles remain highly likely to result in severe casualties—continuing to pose a serious social challenge.

The root causes often extend beyond mechanical failures to systemic issues such as driver fatigue from long hours and risky habits formed under tight delivery schedules. Recognizing these challenges, transportation authorities worldwide have been intensifying safety initiatives—introducing stricter rest-time regulations, reinforcing inspections, and mandating safety features like blind-spot detection systems for large freight trucks.

Today, driver safety is no longer optional—it is a fundamental requirement for corporate sustainability. However, for logistics enterprises operating hundreds or thousands of vehicles, manually supervising every driver is virtually impossible. Faced with this challenge, a leading logistics company in Korea, hereby referred to as “L Company,” turned to Superb AI’s Video Analytics solution to establish a more proactive, data-driven safety monitoring system.

Challenge – Safety Oversight Limited by Time and Manpower

L Company operates over 100 branches and manages a fleet of more than 1,000 service and delivery vehicles. To strengthen driver safety, the company manually reviewed dashcam footage on a quarterly basis. However, this manual process soon revealed several critical issues:

• Excessive Time and Labor Requirements: Reviewing a single vehicle’s dashcam footage took one to two hours on average. With more than 1,000 vehicles in operation, quarterly reviews demanded enormous time and human resources—an unsustainable workload.
• Delayed Response to Unsafe Behavior: Because reviews were conducted only once per quarter, identifying and correcting unsafe driving habits was slow and reactive. Dangerous patterns often went unchecked for months, leaving accident risks unresolved.
• Lack of Objective and Consistent Evaluation: Each reviewer assessed risk differently, resulting in inconsistent evaluations and eroding drivers’ trust in the fairness of the system.
• Rising Financial and Reputational Risks: Every incident carries tangible and intangible costs—from insurance and repair expenses to reputational damage and client trust erosion—amplifying the company’s overall risk exposure.

Solution – Establishing an Automated, AI-Driven Safety Monitoring System

To overcome these challenges, L Company implemented Superb AI’s Video Analytics solution—transforming its conventional, manual process into an intelligent, data-driven, objective system for a fundamental paradigm shift in safety management.

Phase 1: Automatic Selection of Critical Footage

Previously, safety officers had to manually review 6–12 hours of dashcam footage per vehicle. With Superb AI, the system automatically detects and extracts only critical scenes—such as intersections, crosswalk approaches, and entries into school zones—with over 99% accuracy. This allowed managers to focus on critical footage only (less than 5% of the total footage), drastically reducing review time while ensuring that only safety-relevant data was analyzed.

Phase 2: AI-Based Precision Analysis on Potential Violations

Advanced AI models analyzed the selected scenes to detect specific safety rule violations with consistency and precision. For instance, the system identified whether vehicles ran red lights or crossed stop lines by correlating traffic signal status and vehicle speed data. Similarly, it detected failures to stop at crosswalks or slow down in school zones—all based on objective, standardized criteria.

Phase 3: Dashboard for Data-Driven Insights

All analyzed results were presented through an interactive dashboard.

• Driver-Level Insights: Instantly identify repeat violators and provide personalized safety coaching.
• Branch-Level Analysis: Detect patterns—such as frequent signal violations in specific branches—and address potential systemic issues like route design or scheduling.
• High-Risk Area Identification: Pinpoint intersections or road segments with concentrated violations and issue preemptive alerts to drivers.

Through this, L Company’s safety managers were able to shift from instinct-based decisions to data-backed safety planning and training.

Benefit – Safety, Efficiency, and Trust Combined

After adopting Superb AI’s Video Analytics solution, L Company achieved measurable, organization-wide improvements:

• Quantitative Outcomes:

  • Dramatically Reduced Management Time: The manual video review process that once required hundreds of hours per quarter could now be completed within just a few hours.
  • Faster Feedback Cycles: Review frequency increased from quarterly to weekly or monthly, enabling near real-time feedback and corrective training for unsafe driving behaviors.
  • Cost Savings: By reducing potential accident rates, the company lowered insurance premiums, repair costs, and other indirect expenses.

• Qualitative Outcomes:

  • Establishing a Culture of Fair Evaluation: Objective, AI-generated data enabled fair and transparent assessments for all drivers, improving both acceptance and trust in the evaluation process.
  • Raising Safety Awareness Across the Workforce: Frequent, data-driven feedback fostered a stronger safety mindset among drivers, leading to lasting behavioral improvements and a proactive safety culture.
  • Empowering Safety Managers: Freed from repetitive review tasks, safety managers could now focus on higher-value initiatives—such as personalized driver training and strategic improvements to overall fleet safety systems.

Scalability – Evolving Toward Intelligent, Predictive Logistics Management

L Company’s next goal is to evolve from reactive violation detection to proactive prevention—addressing the root causes of accidents in real time. According to Korea Expressway Corporation, 79.2% of highway truck fatalities are linked to drowsy or distracted driving. Preventing these conditions before violations occur is key to sustainable fleet safety.

Leveraging the same proven AI platform, Superb AI enables limitless scalability for future applications:

• Driver State Monitoring: Detects signs of drowsiness—such as blinking frequency, yawning, or head tilting—and issues real-time in-vehicle alerts.
• Distraction Detection: Identifies hazardous behaviors like smartphone use or lack of forward attention during driving to prevent collisions.
• Safety Rule Compliance: Automatically detects basic violations such as unfastened seatbelts, maintaining near-perfect compliance rates across the fleet.

These enhancements elevate fleet management from reactive analysis of past incidents to predictive prevention of future risks—enabling true proactive safety.

AI as the New Standard for Fleet Safety

L Company’s success story sends a clear message: decades-old, manual, and subjective safety management methods are no longer sufficient for today’s complex and fast-moving logistics environments.

In an era where safety management can no longer be dismissed as a cost or manpower issue, proactive, data-driven safety management has become a core competitive advantage—essential for both survival and growth in the logistics industry. Through its collaboration with Superb AI, L Company successfully transformed “accident risk” from a potential liability into a strategic “safety asset.”

Superb AI’s Video Analytics solution breaks down the traditional barriers to AI adoption, empowering logistics companies to move beyond manual oversight and embrace a data-driven future for safety management.

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