V2X, SAE automation levels, occupancy grids, behavior planning, and HD maps connected to perception and control.
This is an editorially selected reading order. Begin at step 1 or go directly to the topic you need.
SAE J3016 is not a score of vehicle intelligence. Learn who performs the dynamic driving task, monitoring, and fallback at Levels 0–5, with ODD, takeover latency, current ADAS, SOTIF, security, and safety evaluation.
An occupancy grid divides space into cells and represents occupied, free, and unknown space probabilistically. Learn Bayesian and log-odds updates, inverse sensor models, ray casting, dynamic decay, 3D voxels, SLAM, costmaps, and planning.
An occupancy grid divides space into cells and represents occupied, free, and unknown space probabilistically. Learn Bayesian and log-odds updates, inverse sensor models, ray casting, dynamic decay, 3D voxels, SLAM, costmaps, and planning.
Behavior planning turns uncertain perception into stop, follow, yield, merge, and lane-change decisions, then constrains path planning and MPC. This primer connects FSMs, behavior trees, POMDPs, current ADAS, safety, and testing.
Behavior planning turns uncertain perception into stop, follow, yield, merge, and lane-change decisions, then constrains path planning and MPC. This primer connects FSMs, behavior trees, POMDPs, current ADAS, safety, and testing.
An HD map represents lanes, boundaries, signs, elevation, and connectivity as machine-readable data. This guide explains how it differs from a SLAM map, how localization works, and why freshness and uncertainty matter.
What do V2V, V2I, V2N, and V2P exchange, and how do DSRC/ITS-G5 and C-V2X differ? This primer connects latency, delivery reliability, cooperative perception, PKI, location privacy, fail-safe design, and reproducible testing.