Mukund P

In this demo we showcase #autonomousdriving off-roads at night, with obstacles avoidance at high-speeds, paving the way for fully #autonomousvehicles, both on- and off-roads. This was our 9th demo off-roads, where earlier last year, in Feb 2024, we showcased the ability to negotiate bidirectional dynamic traffic off-roads. Going forward this year, we will be showcasing our #reinforcementlearning (RL) first approach towards solving the problem of off-roads autonomous driving. Off-roads autonomy is a tough problem to solve, due to non availability of HD maps, and also due to supervised #DeepLearning approach for solving the perception problem, which works well for many of the on-road frontiers, being infeasible from resources point of view. Modern on-road autonomy stacks makes use of HD maps to also embed the motion primitives and to compute avoidance maneuvers on-the-fly, which is not possible off-roads. At @swaayatt, we are thus taking an (inverse-) RL approach towards solving the off-roads autonomous driving problem, where the autonomous agents inherently learn a representation for decision making under uncertainty and motion planning, without requiring an explicit (projected-) representation of the world, as may be computed by different localization and perception modules. We wish everyone a very happy new year!