Rear-end collision escape algorithm for intelligent vehicles supported by vehicular communication

To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle. First published online 18 January 202

An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems

Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.DATA AVAILABLITY STATEMENT : Publicly available datasets were analysed in this study. There data can be found here: https://github.com/TuSimple/tusimple-benchmark and https://boxy-dataset.com/ boxy/ accessed on 25 November 2021.https://www.mdpi.com/journal/sensorsam2023Electrical, Electronic and Computer Engineerin

3D position tracking for all-terrain robots

Rough terrain robotics is a fast evolving field of research and a lot of effort is deployed towards enabling a greater level of autonomy for outdoor vehicles. Such robots find their application in scientific exploration of hostile environments like deserts, volcanoes, in the Antarctic or on other planets. They are also of high interest for search and rescue operations after natural or artificial disasters. The challenges to bring autonomy to all terrain rovers are wide. In particular, it requires the development of systems capable of reliably navigate with only partial information of the environment, with limited perception and locomotion capabilities. Amongst all the required functionalities, locomotion and position tracking are among the most critical. Indeed, the robot is not able to fulfill its task if an inappropriate locomotion concept and control is used, and global path planning fails if the rover loses track of its position. This thesis addresses both aspects, a) efficient locomotion and b) position tracking in rough terrain. The Autonomous System Lab developed an off-road rover (Shrimp) showing excellent climbing capabilities and surpassing most of the existing similar designs. Such an exceptional climbing performance enables an extension in the range of possible areas a robot could explore. In order to further improve the climbing capabilities and the locomotion efficiency, a control method minimizing wheel slip has been developed in this thesis. Unlike other control strategies, the proposed method does not require the use of soil models. Independence from these models is very significant because the ability to operate on different types of soils is the main requirement for exploration missions. Moreover, our approach can be adapted to any kind of wheeled rover and the processing power needed remains relatively low, which makes online computation feasible. In rough terrain, the problem of tracking the robot's position is tedious because of the excessive variation of the ground. Further, the field of view can vary significantly between two data acquisition cycles. In this thesis, a method for probabilistically combining different types of sensors to produce a robust motion estimation for an all-terrain rover is presented. The proposed sensor fusion scheme is flexible in that it can easily accommodate any number of sensors, of any kind. In order to test the algorithm, we have chosen to use the following sensory inputs for the experiments: 3D-Odometry, inertial measurement unit (accelerometers, gyros) and visual odometry. The 3D-Odometry has been specially developed in the framework of this research. Because it accounts for ground slope discontinuities and the rover kinematics, this technique results in a reasonably precise 3D motion estimate in rough terrain. The experiments provided excellent results and proved that the use of complementary sensors increases the robustness and accuracy of the pose estimate. In particular, this work distinguishes itself from other similar research projects in the following ways: the sensor fusion is performed with more than two sensor types and sensor fusion is applied a) in rough terrain and b) to track the real 3D pose of the rover. Another result of this work is the design of a high-performance platform for conducting further research. In particular, the rover is equipped with two computers, a stereovision module, an omnidirectional vision system, an inertial measurement unit, numerous sensors and actuators and electronics for power management. Further, a set of powerful tools has been developed to speed up the process of debugging algorithms and analyzing data stored during the experiments. Finally, the modularity and portability of the system enables easy adaptation of new actuators and sensors. All these characteristics speed up the research in this field

TOWARDS SUSTAINABLE AUTONOMOUS VEHICLES

Ph.DDOCTOR OF PHILOSOPH

An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems

Advanced driver-assistance systems (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. The research proposes an alternative for drivers without having to wait years to gain access to the safety ADAS offers. Wireless Multimedia Sensor Networks (WMSN) for ADAS applications in collaboration with smartphones is non-existent. Intra-vehicle environments cause difficulties in data transfer for wireless networks where performance of such networks in an intra-vehicle network is investigated. A low-cost alternative was proposed that extends a smartphone’s sensor perception, using a camera- based wireless sensor network. This dissertation presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. In addition, to expand on the smartphone’s other commonly available wireless protocols, the Bluetooth protocol was used to collect blind spot sensory data, being processed by the smartphone. Both protocols form part of the Intra-Vehicular Wireless Sensor Network (IVWSN). Essential ADAS features developed on the smartphone ADAS application carried out both lane detection and collision detection on a vehicle. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. Blind spot sensors on the lateral sides of the vehicle provided sensory data transmitted to the smartphone through Bluetooth. IVWSNs are complex environments with many reflective materials that may impede communication. The network in a vehicle environment should be reliable. The network’s performance was analysed to ensure that the network could carry out detection in real-time, which would be essential for the driver’s safety. General ADAS systems use wired harnessing for communication and, therefore, the practicality of a novel wireless ADAS solution was tested. It was found that a low-cost advanced driver-assistance system alternative can be conceptualised by using object detection techniques being processed on a smartphone from multiple streams, sourced from an IVWSN, composed of camera sensors. A low-cost CMOS camera sensors network with a smartphone found an application, using Wi-Fi Direct to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringMEng (Computer Engineering)Unrestricte

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

Stereoscopic 3D dashboards: an investigation of performance, workload, and gaze behavior during take-overs in semi-autonomous driving

When operating a conditionally automated vehicle, humans occasionally have to take over control. If the driver is out of the loop, a certain amount of time is necessary to gain situation awareness. This work evaluates the potential of stereoscopic 3D (S3D) dashboards for presenting smart S3D take-over-requests (TORs) to support situation assessment. In a driving simulator study with a 4 × 2 between-within design, we presented 3 smart TORs showing the current traffic situation and a baseline TOR in 2D and S3D to 52 participants doing the n-back task. We further investigate if non-standard locations affect the results. Take-over performance indicates that participants looked at and processed the TORs' visual information and by that, could perform more safe take-overs. S3D warnings in general, as well as warnings appearing at the participants’ focus of attention and warnings at the instrument cluster, performed best. We conclude that visual warnings, presented on an S3D dashboard, can be a valid option to support take-over while not increasing workload. We further discuss participants’ gaze behavior in the context of visual warnings for automotive user interfaces

Design and evaluation of safety-critical applications based on inter-vehicle communication

Inter-vehicle communication has a potential to improve road traffic safety and efficiency. Technical feasibility of communication between vehicles has been extensively studied, but due to the scarcity of application-level research, communication\u27s impact on the road traffic is still unclear. This thesis addresses this uncertainty by designing and evaluating two fail-safe applications, namely, Rear-End Collision Avoidance and Virtual Traffic Lights