Planning-aligned Token Compression for Long-Context Autonomous Driving

Safe autonomous driving demands that a vehicle remember not just the last few seconds but extended sequences of interactions — a car that cut in two minutes ago, a pedestrian who paused unexpectedly. Processing all that history at full resolution is computationally prohibitive for real-time systems. COMPACT-VA compresses historical context intelligently, guided not just by recency but by what the vehicle actually needs to make upcoming decisions. The gains in speed and memory efficiency, without sacrificing safety-critical information, bring long-horizon autonomous driving closer to practical deployment. This work also has implications for any real-time agent system — robotics, drone navigation — requiring extended situational memory under tight computational budgets. Authors: Zhixuan Liang, Yuxiao Chen, Yurong You, Peter Karkus, Wenhao Ding, Boyi Li, Alexander Popov, Yan Wang, Maximilian Igl, Yiming Li, Danfei Xu, Nikolai Smolyanskiy, Boris Ivanovic, Ping Luo, Marco Pavone Paper: https://arxiv.org/abs/2606.07464v1