DeepSignals- Predicting Intent of Drivers Through Visual Signals.pdf

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2021-05-01

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DeepSignals: Predicting Intent of Drivers Through Visual Signals

Davi Frossard

1,2

Eric Kee

Raquel Urtasun

1,2

Abstract— Detecting the intention of drivers is an essential

task in self-driving, necessary to anticipate sudden events like

lane changes and stops. Turn signals and emergency ﬂashers

communicate such intentions, providing seconds of potentially

critical reaction time. In this paper, we propose to detect these

signals in video sequences by using a deep neural network

that reasons about both spatial and temporal information. Our

experiments on more than a million frames show high per-frame

accuracy in very challenging scenarios.

I. INTRODUCTION

Autonomous driving has risen as one of the most impactful

applications of Artiﬁcial Intelligence (AI), where it has the

potential to change the way we live. Before self-driving cars

are the norm however, humans and robots will have to share

the roads. In this shared scenario, communications between

vehicles are critical to alert others of maneuvers that would

otherwise be sudden or dangerous. A social understanding

of human intent is therefore essential to the progress of self-

driving. This poses additional complexity for self-driving

systems, as such interactions are generally difﬁcult to learn.

Drivers communicate their intent to make unexpected

maneuvers in order to give warning much further in advance

than would otherwise be possible to infer from motion. Al-

though driver movements communicate intent—for example

when drivers slow down to indicate that they will allow a

merge, or drive close to a lane boundary to indicate a desired

merge position—motion cues are subtle, context dependent,

and near-term. In contrast, visual signals, and in particular

signal lights, are unambiguous and can be given far in

advance to warn of unexpected maneuvers.

For example, without detecting a turn signal, a parked car

may appear equally likely to remain parked as it is to pull

into oncoming trafﬁc. Analogously, when a driver plans to

cut in front of another vehicle, they will generally signal

in advance for safety. Buses also signal with ﬂashers when

making a stop to pick up and drop off passengers, allowing

vehicles approaching from behind to change lanes, therefore

reducing delays and congestion.

These everyday behaviors are safe when drivers under-

stand the intentions of their peers, but are dangerous if visual

signals are ignored. Humans expect self-driving vehicles to

respond. We therefore consider in this work the problem

of predicting driver intent through visual signals, and focus

speciﬁcally on interpreting signal lights.

Estimating the state of turn signals is a difﬁcult problem:

The visual evidence is small (typically only a few pixels),

Uber Advanced Technologies Group

University of Toronto

{frossard, ekee, urtasun}@uber.com

Fig. 1: A vehicle, signaling left, passes through occlusion.

The actor’s intent to turn left is correctly detected (left

arrow), including the occlusion (question mark).

particularly at range, and occlusions are frequent. In addition,

intra-class variations can be large. While some regulation

exists, many vehicles have stylized blinkers, such as light

bars with sequential lights in the direction being signaled,

and the regulated frequency of blinking (1.5 ± 0.5 Hz [1])

is not always followed. Furthermore, since we are interested

in estimating intent, vehicle pose needs to be decoded. For

instance, a left turn signal would correspond to a ﬂashing

light on the left side of a vehicle we are following, but on

the other hand would correspond to a ﬂashing light on the

right side of an incoming vehicle. We refer the reader to

Figure 2 for an illustration of some of the challenges of turn

signal estimation.

Surprisingly little work in the literature has considered

this problem. Earlier published works [2], [3] use hand-

engineered features, trained in-part on synthetic data, and

are evaluated on limited datasets. Other approaches have

considered only nighttime scenarios [4], [5]. Such methods

are unlikely to generalize to the diversity of driving scenarios

that are encountered every day.

In this paper, we identify visual signal detection as an

important problem in self-driving. We introduce a large-

scale dataset of vehicle signals, and propose a modern deep

learning approach to directly estimate turn signal states from

diverse, real-world video sequences. A principled network is

designed to model the subproblems of turn signal detection:

attention, scene understanding, and temporal signal detection.

This results in a differentiable system that can be trained end-

to-end using deep learning techniques, rather than relying

upon hard coded premises of how turn signals should behave.

We demonstrate the effectiveness of our approach on a

new, challenging real-world dataset comprising 34 hours of

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