Good news! The future of self driving cars is arriving!
"... MIT researchers have devised a deceptively simple solution to this complicated challenge [of erratic or unpredictably behaving humans when it comes to urban road traffic safety]. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. ...
Their behavior-prediction framework first guesses the relationships between two road users — which car, cyclist, or pedestrian has the right of way, and which agent will yield — and uses those relationships to predict future trajectories for multiple agents.
These estimated trajectories were more accurate than those from other machine-learning models, compared to real traffic flow in an enormous dataset compiled by autonomous driving company Waymo. The MIT technique even outperformed Waymo’s recently published model. ..."From the abstract:
"Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet it remains an open question to jointly predict scene compliant trajectories over multiple agents. The challenge is due to exponentially increasing prediction space as a function of the number of agents. In this work, we exploit the underlying relations between interacting agents and decouple the joint prediction problem into marginal prediction problems. Our proposed approach M2I first classifies interacting agents as pairs of influencers and reactors, and then leverages a marginal prediction model and a conditional prediction model to predict trajectories for the influencers and reactors, respectively. The predictions from interacting agents are combined and selected according to their joint likelihoods. Experiments show that our simple but effective approach achieves state-of-the-art performance on the Waymo Open Motion Dataset interactive prediction benchmark."
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