Off-highway automation rarely fails in ideal conditions, it fails in the edge cases. This presentation explores how rugged autonomy platforms overcome dust, glare, vegetation occlusion and GNSS-denied environments through sensor fusion, radar, lidar and edge AI. The session highlights how Mach’s perception stack enables safe autonomous decision-making in low-visibility terrain, harsh environmental conditions and safety-critical obstacle detection scenarios where conventional optical systems degrade. Real-world deployment examples across mulching, construction and agriculture provide OEMs with a practical roadmap for achieving zero-harm autonomy, improved uptime and resilient perception performance in environments where traditional systems lose reliability.

• How sensor fusion combines radar, lidar, cameras and edge AI to maintain perception in dust, glare and vegetation
• Methods for reliable navigation and obstacle detection in GNSS-denied, low-visibility, safety-critical environments
• How edge AI enables real-time autonomous decision-making when conventional optical systems lose reliability
• Practical lessons from mulching, construction and agriculture deployments in harsh off-highway conditions
• A roadmap for achieving zero-harm autonomy and maximizing uptime in real-world edge cases