arXiv:2606.25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving. Using three Perceiver-based agents that share identical architectures and training data but differ only in their reward configurations$\unicode{x2014}$ranging from basic violation penalties to continuous proximity penalties$\unicode{x2014}$we analyze cross-attention allocation across 50 real-world scenarios from the Waymo Open Motion Dataset. A central methodological finding is that na\"ive pooling of timesteps
The increasing sophistication of AI models and the deployment of autonomous systems necessitate a deeper understanding of their internal decision-making processes, especially concerning safety-critical applications like autonomous driving.
This research provides critical insights into how the design of AI reward functions directly influences agent perception, offering a pathway to building more reliable and interpretable autonomous systems.
Understanding the direct link between reward design and AI attention patterns allows developers to proactively shape what an AI 'sees' and prioritizes, moving beyond purely black-box learning.
- · Autonomous driving companies
- · AI safety researchers
- · Regulatory bodies
- · AI ethics organizations
- · Developers relying solely on black-box reinforcement learning
- · Companies with opaque AI development processes
Improved safety and predictability in autonomous driving AI.
Development of standardized methodologies for evaluating and validating AI attention mechanisms in critical applications.
Enhanced trust in AI systems leading to faster adoption and integration into public infrastructure.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG