Improved Robustness of Crash Avoidance System Sensors to Reduce Maintenance and Repair Costs

NCMS Project #: 141062

Problem: Collision Avoidance Systems (CASs) are designed to prevent or reduce the severity of a collision. These “pre-crash warning systems” combine radar, laser, or camera-based systems to warn drivers of an impending collision by using visual, auditory, or physical cues.  The problem is that many of these devices are mounted in harm’s way with no protection.  Currently CAS systems lack ruggedness.Theyalso do not perform well under a number of common yet adverse road conditions such as glare, fog, heavy rain, snow, accumulation of salt and dirt on the vehicle, and driving through tunnels

Benefit: The benefits of providing a ruggedized, higher performing CAS system for crash avoidance on public highways will improve the ability of the system to perform under extreme environmental conditions and reduce damage to the sensors, vehicles and occupants in the event of collision.

Solution/Approach: A modular tactical vehicle Fire Support Sensor System (FS3) is being developed to provide enhanced situational awareness to soldiers under extreme battlefield conditions.  The FS3 shares many common elements of a commercial CAS system, and as such it can be leveraged to demonstrate improved ruggedization that could be applied to CAS systems in use by the public.  This project will assess the integration of CAS sensors into robust, weatherproof assemblies that allows the sensor to perform their required functions while enduring harsh environments and high collision crashes.

Impact on Warfighter:

  • Increase personal safety
  • Reduce high maintenance, repair and insurance costs
  • Enhanced product reliability
  • Decrease downtime interruptions
  • Increase warfighter mission capabilities and lethality

DOD Participation:

  • Program Manager, Bradley Fighting Vehicle Systems

Industry Participation:

  • CLogic Defense
  • NCMS

Benefit Area(s):

  • Cost savings
  • Repair turn-around time
  • Safety
  • Durability
  • Reliability improvement

Focus Area:

  • Autonomic processes