Edge AI For Real-Time Prediction in Autonomous Vehicles: A Review of Trajectory Forecasting Models and Onboard Optimization

Autonomous vehicles must predict how nearby vehicles, cyclists and pedestrians will move within the next few seconds in order to plan safe actions. Sending raw sensor data to the cloud for this prediction introduces latency and dependence on network connectivity that can jeopardize safety. This paper reviews Edge AI approaches that perform perception and motion prediction on onboard embedded hardware. It surveys trajectory forecasting models, from social pooling of recurrent networks and generative adversarial prediction to graph-based models that encode high-definition maps and agent interactions, and multi-modal predictors that output several likely futures. Large-scale motion forecasting datasets are discussed. The review then examines techniques for running these models within strict latency and power budgets, including pruning, quantization to integer arithmetic, knowledge distillation and hardware–software co-design on automotive accelerators. A five-layer onboard architecture covering sensing, perception, edge prediction, planning and vehicle control is proposed, with research gaps, objectives and a comparison of prediction models. The review concludes that compact, quantized multi-modal predictors running at the edge can deliver the low-latency, reliable forecasts that safe autonomous driving requires.