Predictive AI for Real-Time Autonomous Vehicle Navigation under Uncertainty
Keywords:
Probabilistic Autonomous Navigation, Predictive Decision-Making Under Uncertainty, Real-Time Trajectory Planning, Latency-Constrained Navigation, Stochastic Environment Modeling, Cost-Optimized Manoeuvre Selection, Dynamic Safe-Zone Enforcement, Partial Observability Handling, Sensor Redundancy Integration, Low-Cost Inference Engines, Adaptive Horizon-Based Control, Uncertain Environment Mapping, Disrupted Neighborhood Modeling, Noise-Robust Predictive Control, Autonomous Vehicle Safety, Probabilistic Trajectory Reliability, Online Navigation Optimization, Reactive Perturbation Adaptation, Predictive Control ArchitecturesAbstract
Following a probabilistically-driven design philosophy, the proposed framework aims to address a pivotal aspect of real-time navigation for autonomous vehicles while constantly handling uncertainty—making predictive decisions in an uncertain environment. To this end, given a mapping of the environment augmented with statistical information for all key perturbations impacting performance during online navigation, the questions being tackled are: at each instant during online navigation (substantially constrained by latency requirements), what would be the preferred manoeuvre leading to the most favourable consequence (in terms of an appropriately defined cost function) within a defined disrupted neighbourhood of the current state? Would the definable manoeuvre, coupled with a meaningful understand of the environment at large within an horizon, guarantee that a reliable trajectory through the horizon could be sustained while dynamically adapting to key unexpected changes?
To emphasise the persistent uncertainty within real-time navigation, performance is considered within a continuously dynamically evolving local environmental neighbourhood. The objective is to remain in a safe zone—away from obstacles, poor friction and slim clearance port holes—adapting the predictive decision at the end of a navigation latency to lead reliably through the next required safe region within the horizon while still reacting towards key determined perturbations. In probabilistic terms, this framework thus focuses on a novel predictive-decision scheme capable of overcoming: a unidimensional predictive approach under noise; noise constraints within a partial-observable and dynamic environment. A novel predictive-decision system bridging a low-cost inference engine and a key sensor redundancy has exhibited the capability to reliably adapt to a few sudden—and thus not directly observable—perturbational shifts.
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