Knowledge-Based Approach for the Perception Enhancement of a Vehicle †
2. Related Works
2.1. Vehicular Perception
- Radars have been used for decades for vehicular applications [25,26]. This technology has proved itself to be great in mid-to-long range measurement and have a great accuracy, in addition to doing well in a poor-weather situation . It is still heavily present in vehicles but has a small Field Of View (FOV) and shows poor results in near-distance measurement and static object detection. There is also the problem of receiving interference from other sources or vehicles.
- Cameras have shown an interesting potential, in both single and stereo vision. When considering the perception quality, they are the least expensive sensor that can be used . They allow a quick classification of the obstacle and a potential 3D mapping of the area. Stereoscopy in particular shows very good results in detecting forms, depth, colors and velocity, although it requires substantial computational power . The most advanced models can also be used for long-range precise detection, but they have a more important cost . However, the performance highly depends on the weather and brightness , and the required computational power can sometimes be heavy.
- LIDAR technology relies on measuring laser light reflection to infer the distance to a target. It has been studied since the 1980s  but it is only in early 2000 that it has found its way in vehicular application [31,32]. It is a useful tool for 3D mapping and localization, and can be used on a large FOV , but it relies heavily on good-weather conditions and is not efficient outside a defined range.
2.2. UAV for Vehicular Applications
2.3. Data Fusion
2.4. Secured Communication
- An inclement weather or poor illumination leads to a weakened visibility which is a main factor of car crashes;
- In an environment that is more and more connected, there are some intelligent tools that can be requested to provide additional data to improve perception and visibility, such as UAV;
- Having data from various sensors raises the question of having a mean of fusing them. Knowledge-based approaches, especially ontologies, have shown great potential for multi-sensors management;
- When using an external entity, the communication must be guaranteed to be secured. In that regard, VLC technology offers a strong potential.
3. Proposed Methodology
3.1. Knowledge Base
- Vehicle represents the different; vehicles detected in the environment. The class encompasses both the Car and the UAV entities
- Weather lists all the possible type of weathers that can be encountered. In this case, it covers [Sunny, Fog, Rain, Snow];
- Environment describes the context in which the vehicle it evolves, one amongst [NormalEnv, DarkEnv, BadWeatherEnv, UnusualEnv];
- Sensors covers the sensors that are used for the perception on a vehicle, as detailed in . The main ones are [cameraMono, cameraStereo, cameraInfra, Lidar, Radar, Sonar]. In addition, there are also environmental sensors used to determine the environment status, [rainSensor, brightnessSensor, fogSensor]. It is illustrated in Figure 2 and detailed Table A2 in Appendix A.
3.2. VLC Communication
- Radiofrequency: The DSRC (Dedicated Short (RF)  is the communication protocol designated for automotive V2X use. It is the standard protocol, but the strength of the signal depends of the land form and its sensitivity to electromagnetic interference.
- VLC: The VLC protocol performs poorly in some weather conditions, but it can also improve the lighting in darker areas. It can be an interesting choice.
- Hybrid RF/VLC: This protocol allows a better Quality of Service via the redundancy of information. If the context allows it, this should be the preferred choice.
- Intelligent Transport Systems can natively communicate with their surroundings 
- VLC is a technology revolving around light, making for a brighter environment.
- The redundancy of information allows for a more secured communication and robust system.
3.2.1. Hashing Algorithms
3.2.2. VLC Transmission Speed
4. Simulation and Results
4.1. Simulated Environment
4.2. Logical Rules
- The environmental sensors embedded on the main vehicle will send back some data. If it is above a certain threshold, the environment is inferred as Foggy;
- A Foggy environment is considered as a Bad Weather environment, same as Rainy or Snowy;
- The model looks up for a UAV carrying sensors that works well in this environment and is within reach. If it is deemed acceptable, the UAV is considered for potential data transmission;
- If the proximity condition is fulfilled, a data request is made. Due to VLC performing poorly in heavy fog , an RF-communication protocol is chosen.
4.3. Experimentation Description
- The driver needs to take a turn in an intersection with limited visibility.
- The driver goes through a foggy area.
- The driver needs to go through a certain area where one of the buildings is on fire.
4.4. Results and Discussion
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
|Parameter||Human Eye||Monoscopic Camera||Stereoscopic Camera||Thermal Camera|
|Object detection||Very Good||Good||Very Good||Good for shape detection|
|Object recognition||Very Good||Good||Good||Poor|
|Range detection||Up to 300 m||Poor||Good||Poor|
|Poor weather performance||Poor in snow, fog and heavy rain||Poor in snow, fog and rain||Poor in snow, fog and rain||Good in snow, fog and rain|
|Poor illumination performance||Poor||Poor||Poor||Good|
|Object detection||Very Good||Very Good||Very Good for distance measurement||Very Good|
|Object recognition||Very Good||Good||Poor||Poor|
|Range detection||Up to 300 m||Up to 200 m||Up to 200 m||Very Good|
|Poor weather performance||Poor in snow, fog and heavy rain||Poor in snow, fog and rain||Good||Good|
|Poor illumination performance||Poor||Good||Indepent of illumination||Independent of illumination|
|Active Sensors||Lidar||Uses a Laser in order to map the surroundings|
|Radar||Uses electromagnetic waves in order to determine a distance|
|Ultrasound||Uses ultrasonic waves in order to determine a distance|
|Passive Sensors||Monoscopic Camera||Captures a continuous set of images that can be processed|
|Stereoscopic Camera||Two different Cameras allowing the consideration of depths in image processing|
|Thermal Camera||Capture infrared and thermal emissions. Works in harsher conditions but the results are hard to process|
|Environmental Sensors||Rain Sensor||Determines the Rain situation|
|Fog Sensor||Determines the Fog situation|
|Brightness Sensor||Determines the brightness value (Darkness or Overbright situation)|
|Variable||Ontology Class Values and/or Linking Property||Associated Simulator Value||Comment|
|Vehicle Speed||hasSpeed [NoSpeed,ExtraLowSpeed,LowSpeed,|
|Position of the vehicle||isOnRoad [Roads]||(string) Name of the Road where the vehicle is|
|Distance to Obstacle||hasDistanceFromVehicle [FarDistance,|
|Weather status||[Fog,Sun]||(int) FogSensorValue||Default value “Sun”|
|Brightness status||[Dark,Normal,Overbright]||(int) brightnessValue|
|Environmental Status||[Normal,Dark,BadWeather,Hazardous]||-||Inferred from other elements|
|Hazard||[FireHazard]||(int,int) X & Y Position of the hazard||Not declared if there is no Hazard|
|Sensors available||hasSensor [cameraMono,cameraStereo,cameraInfra,|
|(string) Names of the sensors on the vehicle||For both the car and the UAV|
|Communication protocols||hasCommunicationProtocol[RF,VLC,Hybrid]||(string) Name of the communication protocol|
|UAV data||isActiveUAV [true,false]||-||Inferred from other elements|
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|Class||Object describing the concepts in the domain, whether they are|
abstract ideas or physical actors. Classes can be hierarchized by
levels, for example having a Vehicle as a top-class
containing Car, Bus and Bike as sub-classes
|Individuals||Real instances belonging to Classes and representing|
the actual elements stored in the knowledge base
|Properties||The specific information relative to classes. They can|
be intrinsic to an object, or extrinsic, representing the
interconnections between different concepts and allow
to link two individuals together.
|Study||Speed||Transmission Time (for 200 Gb)|
|Haigh et al., 2013 ||3 Mb/s||18 h|
|Haigh et al., 2016 ||170 Mb/s||19 min|
|Shi et al., 2019 ||5 Gb/s||40 s|
|Average Speed||Time to Complete the Circuit||Max Speed||Number of Incidents|
|Normal driver||27 km/h||67 s||69 km/h||1|
|Cautious driver||16 km/h||92 s||37 km/h||0|
|Careless driver||33 km/h||54 s||78 km/h||3|
|Average Speed||Time to Complete the Circuit||Max Speed||Number of Incidents|
|Normal driver||31 km/h||50 s||78 km/h||0|
|Cautious driver||22 km/h||65 s||46 km/h||0|
|Careless driver||44 km/h||41 s||120 km/h||2|
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Khezaz, A.; Hina, M.D.; Guan, H.; Ramdane-Cherif, A. Knowledge-Based Approach for the Perception Enhancement of a Vehicle. J. Sens. Actuator Netw. 2021, 10, 66. https://doi.org/10.3390/jsan10040066
Khezaz A, Hina MD, Guan H, Ramdane-Cherif A. Knowledge-Based Approach for the Perception Enhancement of a Vehicle. Journal of Sensor and Actuator Networks. 2021; 10(4):66. https://doi.org/10.3390/jsan10040066Chicago/Turabian Style
Khezaz, Abderraouf, Manolo Dulva Hina, Hongyu Guan, and Amar Ramdane-Cherif. 2021. "Knowledge-Based Approach for the Perception Enhancement of a Vehicle" Journal of Sensor and Actuator Networks 10, no. 4: 66. https://doi.org/10.3390/jsan10040066