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1
A different method [160] uses the HSV color space to achieve an accuracy of 86.21%
regarding the rate of identification. This method works best in daylight or with white light
because it takes advantage of the near-color correspondence between white and yellow lane
markers. Consequently, global thresholding is necessary for precisely dividing color planes and
obtaining lane markers in both the YCbCr and HSV color models. In contrast, in tunnel
environments with distinct color illumination, the lane markers diverge from their real hues,
making reliable detection difficult. Sattar et al. [161] developed a different technique called
“SafeDrive,” which identifies the lane marking in areas with poor lighting. They located other
distinct images of highways at the same place using the vehicle’s location information, and they
used those images to identify the lanes. Other researchers [162] also carried out an additional
study that, because of the solid and easily visible lane markings, was able to identify lanes in
several types of weather conditions.
Using already installed roadside webcams, real-time road weather and road state detection
algorithms were developed in [163]. Transfer learning was utilized to train detection models
utilizing three previously learned CNN architectures. When it came to accuracy, ResNet18
outperformed the other models, scoring about 97% for weather and 99% for surface conditions.
These models can be useful in improving road safety since they can automatically recognize
and relate real-time circumstances to road networks without the need for human intervention.
They can be incorporated into advanced traveler information systems (ATIS) for drivers and
utilized to optimize winter maintenance routes. Additionally, the models could replace manual
reporting in snowplows, increasing driver safety and accuracy. Research [164] presents a novel
approach that combines computer vision and DL to extract meteorological data from street-
level photos without any image restrictions. With recognition accuracy ranging from 91% to
95.6% across several categories, the study presents four deep CNN models designed to identify
a variety of visibility circumstances, including dawn and dusk, daytime, evening, glare, and
weather elements like rain and snow. A study [165] presents a DL-based method for lane marker
detection in order to address the difficulties associated with classifying road markers during
rainy weather. Specifically designed for bad weather, the approach gives priority to the best
feature selection from video frames in order to counteract rain-induced blurriness, leading to
adequate classification precision even in difficult weather circumstances. In other research
[166], two different models for AV lane prediction in highway traffic situations are presented.
The study’s main focus is on the use of AI for lane prediction using a sizable dataset from
NGSIM. To showcase the effectiveness of these models, two distinct subsets with 5000 car
samples each were used. The strategy employed a range of classifiers in the Identification
Learner and different methods in Neural Net Fitting, emphasizing the methodology’s
importance in accomplishing good lane prediction without diving into particular accuracy
2
measures.
Due to different illumination circumstances, global thresholds for lane marker
identification frequently produce disappointing outcomes. An efficient lane departure (LD)
technique is presented, enabling lane marker identification that may be used in daylight
highway scenarios as well as tunnel situations with artificial lighting. In order to obtain lane
features, the usual LD technique begins by performing pre-processing to eradicate perspective-
related distortion and isolate an ROI. In order to identify suitable lane markers using color,
shape, alignment, or geometrical measurements using the road scenario data, two categorization
methods, model-based and feature-based, are used [17]. To minimize false positives,
prospective lane markers are further refined and validated using curved or linear lane fitting.
This helps with features like adaptive speed control, automatic lane focusing, and lane departure
alert. LD devices may not be as successful due to a number of issues, such as road obstructions,
ambient lighting sources, and noisy weather. To address these issues, a shadow-resistant
methodology that makes use of Fourier transformation and the maximally stable extreme region
(MSER) approach in the blue color stream is used as a reference [167]. The research findings
[168] suggest that the minimal safe separation between the self-absorbed car and the vehicle
should be used to estimate the extraction of ROI. According to the study, a car traveling at 110
km/h may cover a distance of 35 m with a height of just 150 pixels. Usually, a predetermined
model or set of features is used to construct lane markers using the ROI. In [169], the authors
modified YOLOv3 and presented a brand-new BGRU-Lane (BGRU-L) model; the method
integrates spatial distribution with visual data. High accuracy (90.28 mAP) and real-time
detection speed (40.20 fps) are achieved in difficult settings through integration utilizing the
Dempster–Shafer algorithm, as demonstrated by datasets from KIT, Toyota Technological
Institute, and Euro Truck Simulator 2. The dynamic environment of research and innovation in
the field of road lane detection for autonomous cars is shown by our thorough analysis of the
literature. The abundance of information and developing techniques indicates the ongoing
commitment to increasing the potential of self-governing systems.
6.3. Practical Implications
The practical implementation of DL models for pedestrian and vehicle detection in Avs is
a critical area of research as it directly impacts the safety and reliability of these systems. Studies
such as Kim et al. [145] have demonstrated the effectiveness of CNNs in low-light conditions,
a significant advancement for nighttime AV navigation. Lai et al. [147] have further optimized
the Mask R-CNN algorithm for low-light environments, enhancing detection rates and
computational efficiency. Montenegro et al. [152] have shown that YOLO-v5 can be fine-tuned
to maintain high detection accuracy across varying lighting conditions, ensuring consistent
performance for AVs. Zaman et al. [153] have addressed the challenge of adverse weather
3
conditions, a common obstacle for AVs, by developing DL models that can adapt to weather-
induced distortions, thereby improving system reliability. These studies have identified key
performance indicators (KPIs) such as detection accuracy, false negative rate, computational
efficiency, and real-time processing capabilities, which are essential for evaluating the
practicality of DL models in AVs, as shown in Table 6. The challenges faced, including limited
visibility and dynamic lighting conditions, have been mitigated through model optimization,
data augmentation, and sensor fusion techniques. The insights from these studies are invaluable
for AV developers as they provide a roadmap for creating systems that can operate effectively
in a wide range of environmental conditions, bringing us closer to a future where autonomous
vehicles are a safe and integral part of our transportation infrastructure.
Table 6. Practical implications of DL approaches.
Ref.
Approach
Practical
Implications
KPIs
Challenges
Addressing
Challenges
[145]
CNN-based
Human
Detection at
Nighttime
Enhances pedestrian
detection in low-light
conditions,
improving
safety and navigation
for AVs.
Detection
Accuracy,
False Negative
Rate,
Computational
Efficiency
Limited
visibility,
background
noise
interference
Utilizes CNNs
trained on nighttime
images to
recognize pedestrian
features, potentially
reducing false
negatives.
[147]
Optimized
Mask R-
CNN
for Low-
Light
Pedestrian
Detection
Tailors the Mask R-
CNN algorithm for
low-light
environments,
maintaining high
detection rates.
Detection
Accuracy,
Precision,
Recall,
Real-time
Processing
Optimization
for
low-light
conditions,
computational
complexity
Adjusts model
parameters, uses data
augmentation, and
employs hardware
acceleration for real-
time
processing.
[152]
YOLO-v5
for
Pedestrian
Detection in
Daytime
and
Nighttime
Demonstrates the
versatility of YOLO-
v5
across different
lighting
conditions, ensuring
consistent
performance.
Detection Speed,
Accuracy,
Robustness
Balancing
speed with
accuracy in
varying
lighting
Fine-tunes the
YOLO-v5
model on diverse
datasets, ensuring
generalization across
different lighting
scenarios.
[153]
DL
Approaches
for
Adverse
Weather
Detection
Improves detection
reliability in adverse
weather, crucial for
AV
safety and
functionality.
Detection
Accuracy,
Robustness,
System
Reliability
Degradation
of
performance
due to
weather
conditions
Uses DL models that
learn to recognize
patterns despite
weather
distortions and
integrate
multi-sensor data for
enhanced detection.
7. Discussion, Limitations, and Future Research Trends
7.1. Discussion
Recent literature highlights the increasing use of DL approaches for OD, yielding
promising results under typical conditions. However, there is a pressing need for further
advancements, particularly in adverse weather and complex scenarios. Enhancing OD in such
4
conditions is crucial, especially for AVs, to prevent accidents and ensure safety. In the
following discussion, we delve into the key insights and implications drawn from our research
on OD in adverse weather conditions for AVs. Our research has explored both traditional and
DL methods for AV object detection, focusing on vehicles, pedestrians, and road lanes.
Traditional methods, despite their foundational role, struggle with high computational demands,
slow processing, and occasional misidentification. DL, on the other hand, excels by learning
complex patterns from data, offering faster and more precise detection, especially in
challenging weather conditions. This makes DL a more effective and adaptable solution for AV
systems. Traditional vehicle detection techniques, which rely on appearance- and motion-based
algorithms, face significant challenges in adverse weather conditions. Appearance-based
algorithms like HOG, Haar-like, and SIFT are sensitive to exterior objects, adverse weather,
and lighting variations, making them vulnerable in situations like heavy rain or fog. These
algorithms often struggle to maintain accuracy due to the reduced visibility of vehicle features
and the difficulty in differentiating vehicles from their surroundings. Motion-based detection
methods, which track objects based on movement relative to the camera, also encounter issues
in adverse weather. They can have trouble distinguishing between moving vehicles and other
dynamic elements, such as pedestrians or debris, especially when motion cues are obscured by
weather conditions. DL approaches are more accurate and perform better, as shown in Tables
4 and 5. A comparison of traditional and DL approaches is shown in Table 7.
Table 7. Comparison of traditional and DL approaches.
Ref.
Approaches
Technique
Superiority
Limitations
Additional Considerations
[154,155]
Color-based Lane
Detection
Traditional
Simple to
implement, low
computational cost
Limited in adverse
weather, reliance
on good weather
conditions for
high accuracy
Research into adaptive color
models or fusion with other
sensor data could improve
performance in challenging
conditions.
[158,159]
LiDAR
Integration
Traditional
Reduces reliance
on visual data,
robust in various
weather
High cost,
sensitivity to
unfavorable
weather
conditions.
Cost reduction and
miniaturization of LiDAR
sensors could make this
technology more accessible
for widespread use.
[160]
HSV Color Space
Traditional
Works well in
daylight or with
white light; uses
color
correspondence
Requires global
thresholding, less
effective in tunnel
environments
with distinct color
illumination.
Enhancing the color model
with machine learning
could improve its
adaptability to different
lighting conditions.
[161]
SafeDrive
Technique
Traditional
Utilizes historical
data and vehicle
location for lane
detection in
low-light areas
May not generalize
well to all
environments,
relies on available
historical data.
Incorporating real-time
weather data and vehicle
dynamics could improve
the technique’s robustness.
[163]
SafeDrive
Technique
Traditional
Utilizes historical
data and vehicle
May not generalize
well to all
Incorporating real-time
weather data and vehicle
5
location for lane
detection in
low-light areas
environments,
relies on available
historical data.
dynamics could improve
the technique’s robustness.
[165]
Lane Marker
Detection in
Rainy Weather
DL
Prioritizes feature
selection to
counteract rain-
induced
blurriness
May require
extensive training
data, complex
model architecture
Using smaller, more
specialized models could
reduce computational
demands and improve
real-time performance.
[141]
Generative
Adversarial
Networks (GANs)
DL
Can generate
realistic data for
training, improve
feature extraction
Requires
significant
computational
resources, may
struggle with
certain types of
adverse weather.
Research into GANs for
adverse weather
simulation could provide
more robust training data
for DL models.
[152]
Multispectral
Imaging
DL
Combines different
imaging modalities
for improved
detection
Requires
specialized
hardware; may be
complex to
integrate
Further development of
multispectral imaging
techniques could lead to
more reliable detection
systems.
[163]
Transfer Learning
DL
Allows models to
adapt to new tasks
with fewer data
May not perform as
well as
task-specific
models if the
transfer is not
well-aligned.
Fine-tuning transfer
learning models for specific
AV tasks could enhance
their effectiveness.
Figure 13 presents the overall percentage distribution of papers, while Figure 14 describes
the papers of traditional and DL approaches related to the studied three object detection issues.
From Figure 13, one can see that vehicle detection is frequently studied using traditional and
DL approaches, followed by pedestrian and road lane detection. As shown in Figure 14, DL
approaches were more frequently used for all three issues in AVs compared to traditional
approaches. Prior to 2008, traditional feature extraction methods were prevalent for detection
and classification but had limitations in adverse conditions. Manual feature extraction made
them less suitable for complex applications. DL has become much more popular and prominent
than regular algorithms in recent times. This change in direction is attributed to DL’s
exceptional work, reliable outcomes, and breadth of industry experience. Romero and Antonio
[129] also mainly concentrated on defining DL methods. Peer review has been applied to DL
one-stage and two-stage detectors from the investigations. Currently, the leading methods are
YOLOv8 from the YOLO series and Faster R-CNN from the R-CNN family, renowned for
their superior accuracy and performance. Figure 15 shows the number of summarized papers
for onestage and two-stage detectors, which shows that one-stage detectors are frequently used
for vehicle detection, while, for pedestrian detection, both are equally applied. Two-stage
detectors are frequently used for road lanes compared with single-stage detectors.
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