Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

TL;DR

This paper presents a real-time system that predicts driving maneuvers approximately 3.5 seconds in advance using a novel autoregressive HMM, enhancing ADAS capabilities to prevent accidents more effectively.

Contribution

It introduces an autoregressive input-output HMM for early maneuver prediction using inside and outside vehicle context, improving anticipation accuracy.

Findings

Predicts maneuvers 3.5 seconds before occurrence

Achieves over 80% F1-score in real-time

Evaluated on 1180 miles of diverse driving data

Abstract

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80\% F1-score in real-time.