EV-TTC: Event-Based Time to Collision Under Low Light Conditions

Rapid and accurate dense time-to-collision (TTC) estimation in resource-constrained, low-light environments is challenging for event-based camera systems. Fixed-time event representations like voxel grids face an inherent trade-off: larger temporal windows improve perception accuracy but increase storage demands, while smaller windows reduce storage at the cost of accuracy. We present a hardware-aware TTC estimation system designed for mobile robots, satisfying strict bandwidth, computation, and storage requirements. Our core innovation is a time-scale separation method for computing a multi-temporal scale event representation, achieving a latency of 3.3 ms at 75 Million Events per Second (MEPS). As part of this study, we developed Time-To-Collision/Event Flow ($T^{2}CEF$) a new high-temporal-resolution TTC dataset, using HD event cameras, with temporal estimates at least 7 times greater than existing event datasets such as MVSEC, DSEC, and VECtor via SE(3) interpolation. Our method outperforms existing approaches, reducing mean frame median TTC error by at least 20% compared to voxel grids on $T^{2}CEF$, and achieving an average 31% improvement over other baselines across multiple datasets. Our system runs in real-time on a Jetson Orin NX with just 9.5 ms latency at 141 Hz, outperforming all other methods on embedded hardware, making it ideal for mobile robots.