AI-Powered Trajectory Prediction for Autonomous Systems
KEPT: Knowledge-Enhanced Prediction of Trajectories from Consecutive Driving Frames with Vision-Language Models
This research introduces KEPT, a groundbreaking framework that enhances the safety and reliability of autonomous vehicles. By equipping AI models with a knowledge base of past driving experiences, KEPT enables more accurate, human-like reasoning for predicting vehicle trajectories, especially in complex and unforeseen scenarios.
Executive Impact
The KEPT framework moves beyond simple pattern recognition, creating an AI "co-pilot" that learns from experience. For enterprise fleets, this means a direct and measurable improvement in safety, operational reliability, and a faster path to commercial deployment.
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Reduction in Prediction Error vs. Baseline
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Lower Collision Rate vs. Baseline
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Collision-Free Operation Rate
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Real-Time Knowledge Retrieval
Deep Analysis & Enterprise Applications
Explore the core components of the KEPT framework, its benchmark-setting performance, and the tangible business impact for autonomous vehicle operations.
The KEPT Decision-Making Process
Input Frames
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TFSF Encoding
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Fast Retrieval
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CoT Prompting
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VLM Prediction
Unprecedented Safety and Accuracy
0.07% Collision rate in benchmark tests, setting a new standard for Vision-Language Model-based planners.KEPT's Triple-Stage Training vs. Standard VLM Fine-Tuning
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Enterprise Application: The "Experienced Co-Pilot" for Autonomous Fleets
Scenario: A commercial autonomous trucking company needs to minimize safety incidents in complex urban environments to achieve regulatory approval and reduce insurance premiums.
KEPT's Role: KEPT acts as a digital 'Experienced Co-Pilot'. By retrieving and reasoning over thousands of similar past scenarios in real-time (<1ms retrieval), it provides the core AI with the context needed to navigate tricky situations, like a four-way stop with an unpredictable pedestrian. This is a shift from pure reactive planning to proactive, experience-based decision-making.
Business Outcome: The result is a 32% reduction in critical prediction errors and a significantly lower collision rate. This translates to enhanced safety, a faster path to commercialization, and a more robust, defensible AI driving system.
Estimate Your ROI
Use this calculator to estimate the potential efficiency gains and cost savings by deploying advanced AI trajectory planning in your operations. This models the impact of reducing safety incidents and optimizing fleet movement.
Estimated Annual Savings
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Reclaimed Operational Hours
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Your Implementation Roadmap
Adopting KEPT-like technology is a strategic process. We guide you through a phased approach, from initial data assessment to full-scale deployment and operational optimization.
Phase 1: Scenario Database & Feasibility Study
We analyze your existing telematics and sensor data to build a custom knowledge base of driving scenarios. We identify key "long-tail" events and define the scope for a proof-of-concept.
Phase 2: VLM Fine-Tuning & Simulation
Using the triple-stage methodology, we fine-tune a vision-language model on your specific operational data. Performance is validated in a high-fidelity simulator against your most challenging edge cases.
Phase 3: Pilot Deployment & Safety Validation
The KEPT-powered model is deployed in a limited number of vehicles for closed-course and real-world testing. We focus on safety metrics, disengagement rates, and performance against established benchmarks.
Phase 4: Fleet-Wide Rollout & Continuous Learning
Upon successful validation, the system is rolled out across the fleet. The knowledge base is continuously updated with new driving data, ensuring the model adapts and improves over time.
Ready to Build a Safer, Smarter Fleet?
Let's discuss how the principles behind KEPT can be applied to your specific autonomous driving challenges. Schedule a complimentary strategy session with our AI experts to chart your course towards safer, more reliable autonomous operations.
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