Occupancy Grid Mapping with Cognitive Plausibility
for Autonomous Driving Applications
Alice Plebe
University of Trento

Julian F. P. Kooij
Delft University of Technology

alice.plebe@unitn.it

J.F.P.Kooij@tudelft.nl

Gastone Pietro Rosati Papini
University of Trento

Mauro Da Lio
University of Trento

gastone.rosatipapini@unitn.it

mauro.dalio@unitn.it

Abstract
This work investigates the validity of an occupancy grid
mapping inspired by human cognition and the way humans visually perceive the environment. This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver.
The mechanisms behind human perception could provide
cues on how to improve common techniques employed inautonomous navigation—specifically the use of occupancy
grids to represent the environment. We experiment with aneural network that maps an image of the scene onto an occupancy grid representation, and we show how the model
benefits from two key (and yet simple) changes: 1) a different format of occupancy grid that resembles the way thebrain projects the environment into a warped representation in the cortical visual area; 2) a mechanism similar
to human visual attention that filters out non-relevant information from the scene. These effective expedients canpotentially be applied to any autonomous driving task requiring an abstract representation of the scenario like theoccupancy grids.

1. Introduction
One of the classical cornerstones of artificial intelligence isto draw inspiration from human cognition to design similar intelligent behaviors in artificial systems [2, 15]. This
tenet finds fertile ground in the field of intelligent vehicles
because humans still outperform any state-of-the-art autonomous driving system. While an average human driveris expected to cause a minor accident every 10 million
miles [1], the best autonomous vehicle manufacturers report an average of one disengagement every 30,000 miles[27]. Moreover, the major human causes of traffic acci-

CORTICALMAGNIFICATIONVISUALATTENTION

Figure 1. We propose two cognitively plausible changes to improve occupancy grid mapping: a transformation function mapping the vehicles from the visual scene into an occupancy grid thatis warped in a way resembling the visual cortex (top); a techniquesimulating cognitive visual attention to select important information and mask non-relevant components from a cluttered scene(bottom).

dents are cognitive impairments such as the influence of alcohol or drugs, tiredness, distraction, and recklessness [37].
An expert driver in normal conditions is rarely the causeof an accident. The human ability of driving is especiallyremarkable, given that vehicles are technological artifacts
controlled by interfaces that are extraneous to the natural
human motion control. Nonetheless, humans learn to drive
quickly and robustly.
We argue that the superior human driving capability suggests that the solution to achieving self-driving cars lies in
the human brain itself. The cognitive mechanisms underlying the driving skill could reveal precious insights on how todesign better driving agents and improve the existing techniques commonly employed in autonomous navigation. It
is important to note, however, that vehicles are not biological bodies, and the hardware is not the brain. This is why
most approaches to autonomous driving bear little resemblance to the cognitive processes involved during driving. It
is undeniable that some of these engineering practices—likemodular decomposition—can provide highly desirable features, like reducing the complexity of single sub-modules or
decoupling possible sources of failure [17]. Similarly, there
are algorithms far different from brain computations that arevery effective on silicon processors. Hence, it is crucial tofind a compromise between well-consolidated technologies
and inspiration from human neurocognition.
This work investigates the integration of cognitive plausibility in the task of occupancy grid mapping, i.e., generating from a visual scene a representation of the vehicle’s surrounding environment. We apply cues derived from known
brain mechanisms to improve the format of occupancy representation and to simplify the visual information comingfrom the environment. Our contributions are the following:
1. We modify the occupancy mapping function such thatthe occupancy grid is not uniformly spaced anymore,
but warped in a way that mimics human cortical magnification [18], which is the space warping applied by
the primary cortical visual area to enlarge the central
part of the scene with respect to the peripheral parts;
2. Instead of considering the entire visual scene, we suppress the parts of the image that do not contain crucial elements (vehicles); in this way, we simulate thehuman visual attention mechanisms [10, 20, 26, 35],
which deal with cluttered visual scenes by selecting
important information and by filtering out non-relevant
information.
Note that the cognitive mechanism of visual attention considered here is very different from the popular notion of attention in deep learning—we will clarify this aspect in Section 2.2.
This work provides an example of how key neurocognitive principles can be translated into simple expedients to be
integrated into existing autonomous navigation approaches,
obtaining valid improvements without much computational
effort. Here we show the case of occupancy grid mapping, but the idea of cognitive plausibility has been successfully investigated also in other autonomous driving tasks[6, 30–32].
The rest of the paper is organized as follows. Section 2
summarizes the existing methods of occupancy grid mapping, and it illustrates the different accounts of attention
mechanism in the literature. Section 3 describes our twocontributions: a formulation of occupancy grids that conciliates between mathematical efficiency and cognitive plausibility; a mechanism that emulates the beneficial effects ofvisual attention in perception of driving scenarios. The section also illustrates the model implementation. Section 4
presents the dataset, the evaluation metrics, and the results
on different combinations of input and output formats alongwith a comparison with the related works. Lastly is Section 5 which presents the conclusions.

2. Related Works
2.1. Occupancy Grid Mapping
One of the most established formats of abstract representation of driving environments is the occupancy grids, first
proposed in the field of robotic perception and navigation[12]. An occupancy grid G is a uniformly spaced 2D latticeof binary elements gi,j each representing the presence of an
obstacle at the corresponding patch Ai,j of the continuous
world space:  g_{i,j}=\begin {cases} 1&\text {if}\;\;\exists \;o_k\;|\;\left (X^{(k)},Z^{(k)}\right )\in {A}_{i,j}\\ 0&\text {otherwise} \end {cases}, \label {e:occ_grid_val} (1)
where ok is an object with center X (k) , Y (k) , Z (k) located in the environment (considering the Z-axis pointing
to the travel direction of the ego car, the X-axis pointing to
the right of the ego vehicle, and the Y -axis perpendicular to
the ground plane pointing upwards). The patch Ai,j is thearea on the ground plane defined by a neighborhood of thepoint (Xi , Zj ) of width [∆X, ∆Z].
An occupancy grid can be also interpreted as a binary
image. Assuming the origin of the coordinate system of theimage space in the top-left corner and the bottom-right corner having coordinates (W − 1, H − 1), the transformationof a world point (Xi , Zj ) into image coordinates (i, j) is
the following:
i

=

j

=

W
Xi
+
,
2
∆X
e
Zj − Z
H−
,
∆Z

(2)
(3)

where the parameter Ze represents the longitudinal length of
the “blind zone” of the ego camera, i.e., the area immediately in front of the ego vehicle which lies outside the fieldof view of the camera. Since the information about the obstacles ok is the result of measurements affected by uncertainty, the values of gi,j are usually probabilities rather thansimple binary values.
Probabilistic occupancy grids have become popular also
outside the domain of robotics. They are frequently adopted
in high-level modules of autonomous driving systems—arecent overview of their applications can be found in [25]. A
precious feature of occupancy grids is the predisposition to
be processed by artificial neural networks. Because of their
2D matricial format, they combine effectively with convolutional neural networks. Moreover, the values in a grid cell
can span multiple channels and include additional information, such as the semantic of the obstacle or its velocity.
Most of the works on occupancy grid mapping leverage range sensors like LIDARs and radars, which provide
the depth information to easily generate a bird-eye’s view

(BEV) of the scene. Some works adopt other visual sensors that include depth information, such as RGB-D cameras [16] and stereo cameras [22], while other works fusemultiple sensors, e.g. cameras and LIDARs [13, 28].
There are a few works that map occupancy grids directlyfrom a monocular camera, as in our approach. Lu et al. [23]use a variational encoder-decoder to produce a top-down
semantic representation of the scene. Their work, however,focuses on detecting the drivable areas rather than the vehicles, and it puts special emphasis on training the modelwith weak ground truth. Mani et al. [24] predict both the
BEV layout of the scene and the vehicles location. They
propose to “hallucinate” plausible completions for occluded
parts by leveraging adversarial feature learning. Roddick& Cipolla [36] too generate top-down maps capturing both
the road layout and the traffic participants. They learn animplicit mapping from the image plane to the BEV plane
using the information on the camera geometry. Philion &Fidler [29] produce BEVs that include vehicles, drivableareas, and lane boundaries, leveraging multi-view camera
data coming from a full camera rig covering 6 points ofview. Can et al. [5] predict semantic top-down maps withseveral dynamic and static classes. They exploit additional
information coming from temporal aggregation and feed themodel sequences of frames as input.
In contrast with these works, our approach leverage exclusively single images of the monocular camera—without
the need of camera geometry information, temporal data, or
multiple cameras—and focuses on mapping the occupancyof the vehicles, with emphasis on the accuracy of vehicles
that are close to the ego car.

2.2. Attention Mechanisms
The term “attention” has recently become very popular in
the computer vision community, with the rise of the socalled self-attention vision models. It is important to highlight that these models have little to do with human visual attention. As a consequence, our proposed mechanism
imitating natural attention is profoundly different from thecommon account of self-attention mechanism in deep learning.
The deep learning adaptation of “attention” started
within the field of Natural Language Processing [38], withthe aim to capture long-range dependencies. Self-attention
makes it possible to model long-distance interactions inneural sequence transduction models, even without the needof recurrent layers [38]. By applying a similar approach totasks like image recognition and video classification [39],self-attention allows vision models to augment—or entirely
replace [33]—the convolution operations and capture longrange dependencies in visual data.
Natural visual attention is not about spatial or temporal
long-distance interactions. Although the term encompasses

a set of cognitive mechanisms, it mainly refers to the close
relation between saccadic eye movements and covert orienting of visual spatial attention [10, 20, 26, 35]. Saccades are
rapid movements of the eyes that change the point of foveal
fixation. The saccade location proves to be where the attention of a person is mostly directed; in fact, one cannot movethe eyes to one location and attend to a different one.
The saccadic system has inspired similar mechanisms incomputer vision. For example, De Souza et al. [7] implement a variant of the retinal log-polar transform for a neuralnetwork to detect traffic signs using a small dataset. Notethat there are also computational models that implement
aspects of natural visual attention accurately [8, 9, 19, 21].
However, the purpose of these models is to help investigate the neurocomputational basis of visual attention, andthey are not aimed at engineering applications requiring efficiency and high performance.
We propose an attention mechanism that follows the cognitive account of attention. Given that the task at hand ismapping the location of vehicles in the occupancy grids,
the model needs to direct its “attention” towards the spatial
locations of the vehicles in the input image. We achieve
this by suppressing the visual information not concerning
vehicles.

3. Methodology
This work deals with the task of occupancy grid mapping:given an image of a traffic scene, predict an occupancy
grid of the surrounding vehicles. To realize this task, wehave implemented a deep convolutional encoder-decoder.
The objective of this work is to show that cognitive principles can help design mechanisms to improve predictionof occupancy grids. We propose two cognitively plausible
mechanisms—the first revising the format of the output, thesecond pre-processing the input.

3.1. Cognitively Plausible Occupancy Grids
Two core aspects of occupancy grids go strongly against
cognitive plausibility: the point of view of the image andthe uniform tessellation of the world space. Firstly, an
occupancy grid—as conventionally used in the context ofnavigation—corresponds to a BEV or a top-down view ofthe world space. This kind of orthographic view is clearly
impossible for a human driver. Secondly, in the humanvisual system, the retinal space is never represented uniformly. In the primary cortical visual area, the space warping with respect to the eye view is known as cortical magnification [18]. This warping enlarges the central space of thescene at the expense of the peripheral areas, and it is typically described with the polar-log transformation [3, 11].
We propose an occupancy grid representation that better
resembles the human perception system while preserving

original

ω = 50ω = 10

ω=5

ω=2

ω=1

Figure 2. Warped occupancy grids representing a visual scene (leftmost image) using different values of warping.

the undeniable computational advantages of the simple matricial structure of the grids. By applying a similar warpingto the occupancy grid, we magnify close objects and reduce
the size of distant objects. Just like with cortical magnification, in the “warped occupancy grid”, the more relevant
an object is the more it is represented in detail. In the caseof driving, the relevance of an object depends mainly on thetime required by the ego car to reach it: closer objects are
more crucial, and they require more precision. For this reason, we exploit the idea of cortical magnification in the longitudinal distance only, rather than considering a full polarlog transformation in all dimensions.
We define the warping transformation as a logarithmictransformation in the longitudinal dimension (Z-axis) andas a linear transformation in the lateral dimension (X-axis).
In this way, every element of the warped occupancy gridcorresponds to a square patch of the world space. The longitudinal warping transformation is the following:  w(Z)=\log (Z+\omega ) - \log (\omega ), (4)

where ω is the constant defining the amount of warping,with the maximum deformation at ω = 1.0 and no deformation for ω → +∞. Fig. 2 gives an example of how ω
influences the appearance of a warped occupancy grid. The
transformation of a world point into image coordinates in
the warped occupancy grid is the following:ijW
H∆Z w(Zj ) Xi+,
2w(H∆Z) Zj ∆Xew(Zj ) − w(Z)= H −H.
w(H∆Z)=(5)(6)

This transformation magnifies the objects that are closer tothe ego camera, in contrast with the linear occupancy gridtransformation—eqs. (2) and (3)—in which objects haveconstant size in every point of the image space. The warped
occupancy grid appears more similar to what a human perceives while driving, having a comparable perspective andpoint of view of the scene, and giving more importanceto the objects in the foreground. At the same time, thewarped occupancy grid still corresponds to conventional coordinates in the real world, and it can be potentially used bycontrol systems for navigation without any additional step.

Figure 3. Examples of the attention mechanism applied in three
scenarios: on the first row the original images, on the second row
the results of “focusing the attention” on the vehicles.

3.2. Cognitively Plausible Visual Attention
There are many discrepancies between human perception
and artificial perception, especially in the context of driving. One above all concerns dealing with cluttered visual
scenes. Normally in artificial perception, the system acquires images covering the whole scenario populated by numerous objects, which may be relevant or negligible. Then,elaborate processes analyze the entire images to attempt torecognize the relevant objects and locate them in the worldgeometry.
In human perception, a set of cognitive operations deals
with complex visual scenes by selecting important information and by filtering out irrelevant information. For example, a person driving through the countryside does notneed to first classify the kind of fruits hanging from thetrees by the roadside to understand they are not relevant
information—the driver instinctively directs the attention to
the road and the cars ahead. This cognitive process takes thename of visual attention, and it is one of the most studied
topics in visual science [10, 20, 26, 35].
We propose a mechanism imitating the role of visual attention to reduce the computational complexity of the neural network. When dealing with traffic scenes, the salientparts of the scenes are mainly the vehicles. The attention
should be directed to them, rather than to the surrounding buildings, foliage, or sky—especially in a task like
occupancy grid mapping. Hence, we simulate the effectof attention simply by suppressing the areas of the image
where there are no vehicles, before feeding the image to themodel. Fig. 3 shows the result of this preprocessing. We
use YOLO-v3 [34] to detect in the image the 2D boundingboxes containing vehicles and suppress the pixels outsidethese regions.

3.3. Model Architecture
The proposed model is a deep convolutional neural networkwith a encoder-decoder architecture. The input of the model
is a single image of 800×450 pixels representing the traffic
scene. The output is a graylevel image of 128×128 pixels
that represents the occupancy grid describing the vehicles
location in the scene. The encoder is composed of a stack ofseven convolutions followed by two fully-connected layers,
ending into a latent space of 256 neurons. The decoder consists of a single fully-connected layer followed by a stackof five deconvolutions. The loss function used to train themodel is the binary cross-entropy.
We have implemented different versions of the model
varying the formats of input and output. The two formats of
input are the original frame coming from the video streamof the ego camera (FRM), and the frame processed with theattention mechanism (ATT). The output formats are thestandard uniformly spaced occupancy grid (OCC), and thewarped occupancy grid (WRP). Moreover, we have tested
two versions of WRP with different coefficients of warping:
WRP1 uses ω = 1, and WRP2 uses ω = 2. Ultimately, there
are six variations of the model corresponding to the possiblecombinations of input and output.

4. Results
4.1. Dataset
The dataset adopted in this work is nuScenes [4], developed by the company Motional (formerly known as nuTonomy). The dataset is organized into 1000 video sequences(of which 850 sequences include LIDAR data and annotations) featuring a considerable variety of environments, illuminations, and weather conditions. For a swifter training
and testing process, we reduce the dataset to 200 video sequences (∼40,000 frames). The reduced dataset still preserves a good variety of driving scenarios, but it excludes
the sequences in which there are no vehicles for most of thetime. We randomly allocate 140 of the selected sequences
to the training set, 30 to the validation, and 30 to the test set.
To generate the ground truth data for the occupancy
grids, we map the 3D bounding box annotations onto binary images of 128 × 128 pixels using the two transformation functions defined in equations (2), (3) for the standard
occupancy grid (OCC) and (5), (6) for the warped occupancy
grid (WRP). We set ∆X = ∆Z = 0.5 m so that every pixelof the occupancy grid corresponds to a square of 0.25 m2
e = 3.5 m. Hence, the maximumof the world space, and Z
distance represented in the occupancy grids (both uniform
and warped) is 67.5 m.
To implement the attention mechanism, we use YOLO
as “out-of-the-box” object detector. We find YOLO does

not need fine-tuning on nuScenes because the predicted 2D
bounding boxes appear consistent with the ground truth ofthe dataset.

4.2. Evaluation Metrics
We consider three metrics to evaluate how the cognitive
variations improve the performance of the model. The metrics are the intersection over union (IoU), the average precision (AP), and the distance between centroids in the worldspace. The IoU is computed between the entire target andpredicted occupancy grid images. Instead, the AP and thecentroids are computed with respect to the connected regions extracted from the occupancy grids. The matches between connected regions of target and prediction are determined using the greedy algorithm adopted from KITTI [14].
We set a binarization threshold of θ = 0.4 and NΘ = 40 recall levels for the AP computation.
We are aware that IoU and AP might favor WRP with respect to OCC, because of the different spaces in which themetrics are computed. That is why we have included themetric measuring the distance between centroids. This metric is not usually adopted in works on occupancy grid mapping, but it is computed in terms of the original coordinatesof the world space, so it provides a fair evaluation in thiscontext. In fact, the results in Section 4.3 will show similar
trends in all three metrics.
To better assess how the warped occupancy grid improves the prediction of closer vehicles, we also computethe metrics separately in three different classes of depth in
the world space. The occupancy grids and the related connected regions are partitioned into a close range (CLS) for
depth < 15 m, a far range (FAR) for depth > 30 m, and a
middle range (MID) in between.

4.3. Results
Table 1 shows the results of the proposed model comparing
the six combinations of input and output formats describedin Section 3.3. All model variations share the same architecture and hyperparameters, and they have been trained for
200 epochs. The results are grouped by evaluation metric,
and each group is in turn divided into the four classes ofdepth range described in Section 4.2 (ALL refers to the entire range).
The baseline FRM-OCC maps the image frame into a standard uniformly spaced occupancy grid. The models FRMWRP change the output format as they map the frame into
the warped occupancy grids emulating cortical magnification. The model ATT-OCC employs the cognitive mechanismon the input instead, masking the areas of the image without vehicles. The models ATT-WRP combine both methods.
For the warped occupancy grid, the table reports separate
results for the two warping factors, namely WRP1 and WRP2 .
It is possible to identify some patterns that seem to occur

FRM – OCC
FRM – WRP1
FRM – WRP2
ATT – OCC
ATT – WRP1
ATT – WRP2

ALL

IoU ↑
CLS
MID

FAR

ALL

16.4
30.3
31.3
20.3
32.9
34.0

42.0
47.0
46.6
48.5
53.9
53.7

12.2
8.7
9.1
14.9
13.6
15.2

0.077
0.096
0.089
0.113
0.142
0.144

26.0
30.9
33.4
30.1
34.1
38.1

Average Precision ↑
CLS
MID
FAR
0.195
0.254
0.231
0.319
0.416
0.414

0.103
0.085
0.096
0.154
0.148
0.175

0.022
0.005
0.009
0.034
0.021
0.027

Distance Centroids ↓
CLS
MID
FAR

ALL
1.74
1.51
1.15
1.58
1.15
0.99

1.41
1.24
1.04
1.39
0.95
0.98

1.80
1.62
1.36
1.61
1.39
0.97

2.11
5.18
4.25
2.06
4.56
3.96

Table 1. Comparison of the formats of input and output over the same architecture. The scores are computed on the entire output and on
partitions of the output space based on the distance from the ego car.

Input (FRM)

Target (OCC)

OutputInput (FRM)

Target (OCC)

OutputInput (FRM)

Target (OCC)

OutputInput (ATT)

Target (WRP)

OutputInput (ATT)

Target (WRP)

OutputInput (ATT)

Target (WRP)

OutputFigure 4. Visual results on six test samples using the baseline (FRM-OCC) and the model with cognitive plausibility (ATT-WRP2 ). The
predicted output is displayed with a binarization threshold of 0.4.

consistently. Firstly, the models with input ATT outperformthe models with the corresponding output and input FRM.
This happens consistently in all the metrics. It can be deduced that the proposed attention mechanism benefits thetraining of the neural network. Secondly, the models that
perform better in the close and middle ranges (CLS and MID)are the models with ouput WRP. This happens, again, in all
three metrics. The superiority of WRP in the middle/close
range is coherent with the idea that the warping mimics theeffect of cortical magnification and improves the accuracyof the vehicles in the proximity of the ego car. Moreover,while the strongest warping factor WRP1 achieves the best
CLS scores, the model with WRP2 performs better in general,
which is indicated by the higher ALL scores in all metrics.
Lastly, the models with WRP manifest a larger gap between
the scores in CLS and FAR, with respect to the model using OCC as output. This is, once again, consistent with thefact that the standard occupancy grid is uniformly spaced.
Hence, a car close to the camera (which occupies a large

portion of the image frame) and a very distant car (displayedin few pixels of the frame) occupy a similar number of gridelements and are predicted with similar accuracy. Fig. 4
shows visual results on six test samples using the baseline
FRM-OCC (top of figure) and the best model with cognitiveplausibility ATT-WRP2 (bottom).
Table 2 presents a comparison with the other relevant
approaches mapping visual input into occupancy grids reviewed in Section 2.1. We have selected the work that havetrained their models on nuScenes. Table 2 reports the IoU
scores provided in the original papers. Although all methods employ nuScenes, the video sequences in the training
and test sets are not the same in all cases. Moreover, it
should be recalled that we train our models on a reduced
number of nuScenes sequences, as described in Section 4.1.
Hence, this comparison is to be considered in broad terms.
The combination of the two proposed mechanisms (ATTWRP2 ) drastically improve the performance of the baseline(FRM-OCC). These cognitive-inspired mechanisms are easy

IoU

References

Lu et al. [23]Roddick & Cipolla [36]Philion & Fidler [29]Can et al. [5]

8.8
24.7
32.1
36.0

Ours (FRM-OCC)
Ours (ATT-WRP2 )

16.4
34.0

[1] U.S. National Highway Traffic Safety Administration. Traffic safety facts annual report tables, 2018. 1[2] Margaret Boden. AI: Its nature and future. Oxford University
Press, Oxford (UK), 2016. 1[3] Richard Born, Alexander R. Trott, and Till S. Hartmann.
Cortical magnification plus cortical plasticity equals vision?
Vision Research, 111:161–169, 2015. 3[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora,
Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuScenes: A multimodal dataset for autonomous driving. In Proc. of IEEE
International Conference on Computer Vision and Pattern
Recognition, pages 11621–11631, 2020. 5[5] Yigit Baran Can, Alexander Liniger, Ozan Unal, Danda
Paudel, and Luc Van Gool. Understanding bird’s-eye view
semantic hd-maps using an onboard monocular camera.
arXiv preprint arXiv:2012.03040, 2020. 3, 7
[6] Mauro Da Lio, Riccardo Donà, Gastone Pietro Rosati Papini,and Kevin Gurney. Agent architecture for adaptive behaviors
in autonomous driving. IEEE Access, 8:154906–154923,
2020. 2
[7] Alberto F De Souza, Cayo Fontana, Filipe Mutz, Tiago Alvesde Oliveira, Mariella Berger, Avelino Forechi, Jorcy deOliveira Neto, Edilson de Aguiar, and Claudine Badue. Traffic sign detection with vg-ram weightless neural networks.
In The 2013 International Joint Conference on Neural Networks (IJCNN), pages 1–9. IEEE, 2013. 3[8] Gustavo Deco. Biased competition mechanisms for visual attention in a multimodular neurodynamical system. In Stefan
Wermter, Jim Austin, and David Willshaw, editors, Emergent
neural computational architectures based on neuroscience:
towards neuroscience-inspired computing, pages 114–126.
Springer-Verlag, Berlin, 2001. 3[9] Gustavo Deco and Edmund Rolls. A neurodynamical cortical model of visual attention and invariant object recognition.
Vision Research, 44:621–642, 2004. 3[10] R. Desimone. Neural circuits for visual attention in the
primate brain. In G. A. Carpenter and S. Grossberg, editors, Neural Networks for Vision and Image Processing. MIT
Press, Cambridge (MA), 1992. 2, 3, 4
[11] Robert O. Duncan and Geoffrey M. Boynton. Cortical magnification within human primary visual cortex correlates withacuity thresholds. Neuron, 38:659–671, 2003. 3[12] Alberto Elfes. Using occupancy grids for mobile robot perception and navigation. Computer, 22:46–57, 1989. 2[13] Özgür Erkent, Christian Wolf, Christian Laugier,David Sierra Gonzalez, and Victor Romero Cano. Semantic grid estimation with a hybrid Bayesian and deepneural network approach. In International Conference onIntelligent Robots and Systems, pages 1–8, 2018. 3[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we
ready for autonomous driving? the KITTI vision benchmark
suite. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pages 3354–3361,
2012. 5

Table 2. IoU scores (%) on the nuScenes dataset. We report thescores from the original papers.

to implement and employ a light architecture easy to inspect. The related approaches adopt heavier neural networks which exploit additional information, like temporal
data [5], multiple cameras [29], and camera geometry [36].
On the other hand, these works do not focus specifically on
the vehicles: they also predict semantic maps of the roadand the drivable areas. Therefore, the results in Table 2
are, once again, an approximate comparison. Nonetheless,while the baseline (FRM-OCC) obtains limited performance,
the model with cognitive plausibility (ATT-WRP2 ) is competitive compared to most of the state-of-the-art methods.

5. Conclusions
We have investigated the benefit of integrating cognitiveplausibility into autonomous driving tasks, specifically inthe case of occupancy grid mapping. Our method improves
the baseline approach to occupancy grid mapping by introducing two cognitively plausible changes. First, we havesimulated the cognitive mechanism of visual attention by
suppressing in the visual input the areas of the image that
do not contain vehicles. Second, we have mimicked humancortical magnification by warping the occupancy grid in order to magnify close vehicles and improve their accuracy.
The two improvements require minimum implementation
effort compared to other related state-ot-the-art approaches,
obtaining competitive results on the nuScenes dataset. Our
results indicate the value of taking human cognition as asource of inspiration—an idea that could be explored in different tasks for intelligent vehicles.
There is a fundamental discrepancy between our approach and the cognitive mechanisms involved during driving: the lack of dynamic information. Natural vision is inherently dynamic. Humans do not perceive static independent “frames”; on the contrary, they rely heavily on temporal dynamics of the environment. The current work does
not leverage information about the movements of the objects. Hence, a promising future development could be a
way to integrate dynamic information in the model to bettersupport the cognitive inspiration.

[15] Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. Neuroscience-inspired artificial intelligence. Neuron, 95:245–258, 2017. 1[16] Marian Himstedt and Erik Maehle.
Online semantic mapping of logistic environments using rgb-d cameras. International Journal of Advanced Robotic Systems,
14(4):1729881417720781, 2017. 3[17] Yu Huang and Yue Chen. Autonomous driving with deeplearning: A survey of state-of-art technologies. arXiv,
abs/2006.06091, 2020. 2[18] David Hubel and Torsten Wiesel. Uniformity of monkey striate cortex: a parallel relationship between field size, scatter,and magnification factor. Journal of Comparative Neurology, 158:295–305, 1974. 2, 3
[19] Laurent Itti and Christof Koch. Computational modelling ofvisual attention. Nature Reviews Neuroscience, 2:194–203,
2001. 3[20] Nancy Kanwisher and Ewa Wojciulik. Visual attention: insights from brain imaging. Nature Reviews Neuroscience,
1:3310–3318, 2000. 2, 3, 4[21] Sofia Krasovskaya and W. Joseph MacInnes. Salience models: A computational cognitive neuroscience review. Vision,
3:vision3040056, 2019. 3[22] You Li and Yassine Ruichek. Occupancy grid mapping inurban environments from a moving on-board stereo-visionsystem. Sensors, 14(6):10454–10478, 2014. 3[23] Chenyang Lu, Marinus Jacobus Gerardus van de Molengraft, and Gijs Dubbelman. Monocular semantic occupancy grid mapping with convolutional variational encoder–decoder networks. IEEE Robotics and Automation Letters,
4:445–452, 2019. 3, 7
[24] Kaustubh Mani, Swapnil Daga, Shubhika Garg, Sai Shankar
Narasimhan, Madhava Krishna, and Krishna Murthy Jatavallabhula. Monolayout: Amodal scene layout from a single image. In Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision, pages 1689–1697, 2020. 3[25] Sajjad Mozaffari, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul
Jennings, and Alexandros Mouzakitis. Deep learning-based
vehicle behavior prediction for autonomous driving applications: A review. IEEE Transactions on Intelligent Transportation Systems, early access:1–15, 2020. 2[26] Scott O. Murray and Sheng He. Contrast invariance in thehuman lateral occipital complex depends on attention. Cerebral Cortex, 16:606–611, 2001. 2, 3, 4[27] California’s Department of Motor Vehicles. Disengagement
report, 2021. 1[28] Sang-Il Oh and Hang-Bong Kang. Fast occupancy grid filtering using grid cell clusters from lidar and stereo vision sensor
data. IEEE Sensors Journal, 16(19):7258–7266, 2016. 3[29] Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encoding
images from arbitrary camera rigs by implicitly unprojecting
to 3d. In European Conference on Computer Vision, pages
194–210. Springer, 2020. 3, 7[30] Alice Plebe and Mauro Da Lio. On the road with 16 neurons:
Towards interpretable and manipulable latent representations
for visual predictions in driving scenarios. IEEE Access,
8:179716–179734, 2020. 2[31] Alice Plebe and Mauro Da Lio. Neurocognitive-inspired approach for visual perception in autonomous driving. In Communications in Computer and Information Science, volume1217, pages 113–134. Springer, Cham, 2021. 2[32] Alice Plebe, Riccardo Donà, Gastone Pietro Rosati Papini,and Mauro Da Lio. Mental imagery for intelligent vehicles.
In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS),
pages 43–51. Science and Technology Publications, 2019. 2[33] Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan
Bello, Anselm Levskaya, and Jonathon Shlens. Stand-alone
self-attention in vision models. In 33rd Conference on Neural Information Processing Systems, 2019. 3[34] Joseph Redmon and Ali Farhadi. YOLOv3: An incremental
improvement. arXiv, abs/1804.02767, 2018. 4[35] John H. Reynolds, Leonardo Chelazzi, and Robert Desimone. Competitive mechanisms subserve attention in
macaque areas V2 and V4. Nature Neuroscience, 2:1019–
1025, 1999. 2, 3, 4[36] Thomas Roddick and Roberto Cipolla. Predicting semantic map representations from images using pyramid occupancy networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages
11138–11147, 2020. 3, 7[37] Santokh Singh. Critical reasons for crashes investigated in
the National Motor Vehicle Crash Causation Survey. Technical Report DOT HS 812 115, National Highway Traffic
Safety Administration, Washington (DC), 2015. 1[38] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia
Polosukhin. Attention is all you need. In Advances in Neural
Information Processing Systems, pages 6000–6010, 2017. 3[39] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In
International conference on machine learning, pages 7354–
7363. PMLR, 2019. 3