What Is PACE‑AI and How It Works
Coventry University’s Research Centre for Future Transport and Cities has developed PACE‑AI, an artificial‑intelligence system designed to calculate vehicle and pedestrian speeds in collision incidents. By feeding a few observable details—such as the height of a pedestrian, the extent of damage to a vehicle’s bumper or windscreen—officers can receive an instant estimate of the vehicle’s speed, the pedestrian’s crossing gait, and the direction of movement. The tool is built on a combination of biomechanics, machine‑learning algorithms, and real‑world collision data, allowing it to produce results in seconds at the scene.
Key Input Parameters
- Pedestrian height and posture
- Vehicle damage measurements (bumper, windscreen, side panels)
- Road geometry and surface conditions
- Time of day and lighting conditions
Once the data is entered, PACE‑AI processes the information and outputs a speed estimate with a confidence interval. The system also flags potential variables that could affect accuracy, such as weather or road surface, and suggests additional data that could refine the calculation.
Benefits for Police Investigations
Accurately determining the speed of a vehicle in a collision is traditionally a time‑consuming process that may involve manual measurements, witness statements, and post‑incident reconstruction. PACE‑AI streamlines this workflow by:
- Reducing the time required to generate a speed estimate from hours to seconds.
- Providing a data‑driven baseline that can be cross‑checked against other evidence.
- Improving the consistency of speed calculations across different officers and jurisdictions.
- Enabling quicker decisions about the need for further forensic analysis or court testimony.
In hit‑and‑run cases, where the vehicle may have left the scene before officers arrived, PACE‑AI can still offer a credible estimate based solely on the damage and pedestrian data available at the scene. This can be pivotal in establishing liability and guiding subsequent investigative steps.
Implementation Steps for Police Departments
Adopting PACE‑AI requires a structured approach to ensure seamless integration into existing investigative protocols. The following steps outline a practical roadmap:
1. Pilot Program with Training
Start with a small pilot involving a dedicated team of officers. Provide hands‑on training sessions that cover:
- Data collection techniques (accurate measurement of damage, pedestrian height, etc.)
- Using the PACE‑AI interface and interpreting output.
- Understanding the tool’s limitations and confidence intervals.
2. Integration with Incident Reporting Systems
Embed PACE‑AI into the digital incident reporting platform used by your department. This allows officers to input data directly into the system, automatically generating a speed estimate that is logged alongside other evidence.
3. Quality Assurance and Calibration
Periodically compare PACE‑AI outputs with traditional reconstruction results to validate accuracy. Adjust calibration settings if necessary, especially when operating in different geographic or climatic conditions.
4. Documentation and Standard Operating Procedures
Update your department’s SOPs to include PACE‑AI usage guidelines, data entry protocols, and reporting formats. Ensure that all officers are aware of the tool’s role and how to cite its results in official documents.
5. Feedback Loop with Coventry University
Maintain an open line of communication with the research team. Share anonymized case data to help refine the algorithm and contribute to ongoing improvements.
Potential Applications Beyond Forensics
While PACE‑AI is currently focused on police investigations, its underlying technology has broader implications:
- Medical Triage: By estimating collision speed, paramedics can anticipate injury patterns—such as the likelihood of traumatic brain injury—and prioritize treatment protocols.
- Insurance Claims Processing: Insurers can use speed estimates to assess fault and expedite claim settlements.
- Urban Planning: City planners can analyze collision data to identify high‑risk zones and implement targeted safety measures.
- Automotive Safety Research: Manufacturers can study real‑world collision speeds to improve vehicle design and occupant protection systems.
Future Directions and Research Opportunities
Coventry University is already exploring extensions to PACE‑AI, including:
- Integration with traffic camera feeds for real‑time speed estimation.
- Expansion to multi‑vehicle collisions and complex traffic scenarios.
- Development of a mobile application for first responders in remote areas.
- Collaboration with medical research institutions to correlate speed data with injury outcomes.
These initiatives aim to broaden the tool’s applicability and enhance public safety across multiple sectors.
Getting Involved with Coventry University Research
Police departments, forensic analysts, and researchers interested in PACE‑AI can engage with Coventry University through several channels:
- Contact the Research Centre for Future Transport and Cities to discuss partnership opportunities.
- Attend upcoming webinars and workshops hosted by the university’s AI research group.
- Apply for collaborative research grants that focus on AI in public safety.
- Explore the university’s open‑access publications on AI‑driven collision analysis.
By collaborating with Coventry University, law‑enforcement agencies can stay at the forefront of AI innovation and contribute to the development of tools that enhance investigative efficiency and public safety.
Call to Action
Ready to explore how PACE‑AI can transform your investigative workflow? Learn more about Coventry University’s research initiatives and connect with the team behind PACE‑AI today.
Have questions about integrating AI into your department’s protocols? Contact the research centre for a detailed discussion.
Share your experiences with AI tools in policing in the comments below and join the conversation on how technology can improve investigative outcomes.
