Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps

TLDR

Autonomous driving systems depend on detailed prior maps, but building, storing, and maintaining such maps is difficult and impractical in large rural areas, limiting the technology's widespread applicability. The paper proposes a mapless driving framework that combines sparse topological maps for global navigation with sensor‑based perception for local navigation to enable autonomous driving in rural environments. The framework selects a local waypoint within sensor view, generates a road‑rule‑compliant trajectory, and updates it using odometry and uncertainty estimates via least‑squares residuals and recursive filtering, enabling reliable high‑speed navigation without detailed prior maps. The system was demonstrated on a full‑scale autonomous vehicle in a challenging rural setting and benchmarked on extensive collected data, showing its effectiveness.

Abstract

State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology, which has the potential for an incredibly positive societal impact. In this paper, we address the problem of autonomous navigation in rural environments through a novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation. First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed. These trajectories are updated to remain in the local frame using the vehicle's odometry and the associated uncertainty based on the least-squares residual and a recursive filtering approach, which allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps. We demonstrate the performance of the system on a full-scale autonomous vehicle navigating in a challenging rural environment and benchmark the system on a large amount of collected data.