arXiv:2605.23270v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) models capture interaction-aware temporal dependencies via causal factorization, but their step-wise decoding leads to error accumulation and suboptimal global structure. In contrast, diffusion models optimize trajectories globally but lack explicit causal constraints, making them unreliable in interactive and safety-critical scenarios. This dichotomy reveals a deeper issu
The proliferation of advanced vision-language models and the increasing complexity of autonomous systems are driving the need for more sophisticated planning algorithms that balance causal reasoning and global consistency.
This research addresses a fundamental limitation in current autonomous driving systems, which, if solved, could accelerate the deployment and improve the reliability of self-driving vehicles and other robotic applications.
The ability to integrate causal temporal reasoning with global trajectory optimization could lead to safer, more robust, and more human-like autonomous decision-making, moving beyond the limitations of purely autoregressive or diffusion models.
- · Autonomous vehicle developers
- · Robotics companies
- · AI software providers
- · Logistics and transportation industries
- · Companies reliant on less sophisticated planning algorithms
- · Manual labor in transportation and logistics
Improved performance and safety metrics for autonomous vehicles and robotic systems.
Faster adoption and regulatory approval for autonomous technologies due to enhanced reliability.
Significant economic reconfigurations in sectors heavily dependent on transportation and mobile robotics.
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Read at arXiv cs.AI