Design and Development of an Autonomous Car using Object Detection with YOLOv4

Future cars are anticipated to be driverless point-to-point transportation services capable of avoiding fatalities To achieve this goal auto-manufacturers have been investing to realize the potential autonomous driving In this regard we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering on a track made of colored cones This paper goes through the process of fabricating a model vehicle from its embedded hardware platform to the end-to-end ML pipeline necessary for automated data acquisition and model-training thereby allowing a Deep Learning model to derive input from the hardware platform to control the car s movements This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction This paper presents a Computer Vision model that learns from video data and involves Image Processing Augmentation Behavioral Cloning and a Convolutional Neural Network model The Darknet architecture is used to detect objects through a video segment and convert it into a 3D navigable path Finally the paper touches upon the conclusion results and scope of future improvement in the technique use

Connected and Automated Vehicle Based Intersection Maneuver Assist Systems (CAVIMAS) and Their Impact on Driver Behavior, Acceptance, and Safety

Intersection crashes can be potentially mitigated by leveraging deployments of vehicle-to-infrastructure (V2I) and vehicle-to- vehicle (V2V) safety management solutions. However, it is equally critical that these deployments are undertaken in tandem with interventions based on human factors evidence relating to the content and presentation of such solutions. This driving simulator study designed and evaluated a conceptual system - Connected and Automated Vehicle based Intersection Maneuver Assist Systems (CAVIMAS) - aimed at assisting drivers with intersection maneuvers by leveraging connected infrastructure and providing real-time guidance and warnings and active vehicle controls. Results indicate that human factors considerations for the design and deployment of such systems remain paramount, given the findings related to drivers’ trust and acceptance of these systems as measured via surveys and by examining actual driving behaviors.Center for Connected and Automated Transportationhttps://deepblue.lib.umich.edu/bitstream/2027.42/156048/4/Connected_and_Automated_Vehicle_Based_Intersection_Maneuver_Assist_Systems_CAVIMAS.pd

Studying Driver Behavior in Self Driving Cars Using a Driving Simulator

Undergraduate Research Opportunity Program (UROP)http://deepblue.lib.umich.edu/bitstream/2027.42/116120/1/Studying_Driver_Behavior_SelfDrivingCars.pd

Comparison of Trained and Untrained Novice Drivers’ Gaze Behavior in Risky and Non-Risky Scenarios

PC-based training programs have been developed that have been shown to improve novice drivers’ hazard anticipation skills. Such programs give novice drivers information about particular driving situations (scenarios) where hidden threats could appear. We wanted to know whether this improvement in trained novice drivers’ scanning skills was simply because the trained drivers were scanning more in general or, instead, were scanning more specifically in the scenarios in which potential threats could appear. In order to evaluate this question, we trained 11 novice drivers using a PC-based program and then compared their hazard anticipation performance on a driving simulator with the hazard anticipation performance of 11 untrained novice drivers. The drivers’ eye movements were recorded for the duration of the drives. The glances of the drivers to the right (the correct response in most of the risky scenarios) were analyzed for each of the relevant risky scenarios and for stretches of non-risky situations. The trained drivers did look to the right 6.5% more in the non-risky situations than did the untrained drivers, although the difference was far from significant. However, the trained drivers looked to the right 32.7% more in the risky scenarios than in the non-risky situations, indicating they were discriminating quite well between the two situations. The untrained drivers also showed a smaller, but significant, discrimination between the risky scenarios and non-risky situations, as they looked to the right 18.9% more in the risky scenarios than in the non-risky stretches

The Effects of PC-Based Training on Novice Drivers\u27 Risk Awareness in a Driving Simulator

Novice drivers are almost nine times more likely to die in a crash thanmore experienced drivers. This increased risk has been found to be largely due tonovice drivers’ inability to predict the risks in the roadway ahead. A PC-basedRisk Awareness and Perception Training Program (RAPT) was developed toteach novice drivers about different categories of risky situations likely to beencountered while driving. The format was an interactive multimedia presentationwith both plan (i.e., top down) views and perspective views of roadway geometrythat illustrated generally risky scenarios along with information about the type ofrisks and the relevant areas that attention should be allocated to in order to detectthe risks. A set of novice drivers was trained with this program. The eyemovementsof the participants were then evaluated in a driving simulator todetermine whether areas of potential risk were fixated, and their performance wascompared to a separate set of untrained novice drivers. The ability of the novicedrivers to identify risks in static views improved after they completed the trainingprogram. More importantly, the trained novice drivers were significantly morelikely to correctly fixate on risk relevant areas in the simulated drivingenvironment than the untrained drivers 3-5 days after training

Hazard Perception and Distraction in Novice Drivers: Effects of 12 Months Driving Experience

The high crash risk of novice drivers has been partly attributed to their underdeveloped hazard perception abilities. Novice drivers also have an increased risk of crashes due to distractions. Studies show that novice drivers do not detect risk relevant cues and are more susceptible to distractions when compared to adult drivers. This test track study was conducted to study the effects of 12 months of driving experience on teenagers. Forty-two teenagers and their parents drove through hazard perception scenarios while engaged in secondary tasks. These participants had participated in a similar session 12 months earlier. For the odometer and texting task conditions the novice drivers showed an improvement in hazard perception and a small but insignificant decrease in task suspension after 12 months. For the scenario with the cell phone task none of the novice drivers suspended the task, nor exhibited any sort of hazard perception behavior at 12 months. The results indicate that although hazard perception generally improves with experience under some distracting task conditions this is not the case for cell phone distractions

Effects of Behavior-Based Driver Feedback Systems on Commercial Long Haul Operator Safety

There are large economic and societal costs to commercial motor vehicle crashes. A majority of crashes are precipitated due to driver-related factors. Behavior-based systems that influence drivers with feedback from safety managers can help reduce driver-related risk factors. These systems harness the experience and knowledge of managers along with advanced driver telematics that monitor and record driver behaviors to positively influence driver safety. Safety solutions that focus on modifying driver behaviors thus hold promise for improving the safety record of commercial trucking. In this study, one such feedback system was examined by analyzing data from a commercial trucking fleet, treating the system deployment as a natural experiment. This made it possible, without experimental intervention, to compare drivers before and after system introduction, and to compare drivers that were subject to this system with those that drove with no supervisor feedback. Adverse event data were obtained for drivers in the fleet and weekly event rates were calculated taking into account driving exposure (in miles). Results show that drivers improved after receiving safety feedback and significantly more so than drivers that did not receive feedback

Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.Comment: 42 pages, 5 figures, 3 Table

Pre-Frontal Cortex Activity of Male Drivers in the Presence of Passengers During Simulated Driving: An Exploratory Functional Near- Infrared Spectroscopy (fNIRS) Study

Adolescents are more likely to be involved in motor vehicle crashes in the presence of peer passengers, and risky driving behaviors of male teenagers increase in the presence of male peer passengers. There could be several mechanisms of the influence of peer passengers, however it is evident that the male teenage driver with a male peer passenger makes riskier decisions than when alone. The developing teenage brain’s activity is different from that of adults during decision-making, especially in regions associated with impulse control, response inhibition, and risk taking. This study tested the feasibility of using functional nearinfrared spectroscopy (fNIRS), a non-invasive brain imaging method that allows in vivo measurements of oxygenated and deoxygenated hemoglobin in cortical tissue, to study drivers’ brain activation during simulated driving. Cortical activity was measured in participants driving alone and in the presence of a passenger. When at a dilemma zone at a signalized intersection participants showed increased activation in regions of the left and right medial pre-frontal cortex when driving with a passenger as compared to when driving alone