Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S

2017 ◽  
Vol 11 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Model Development ◽  
Autonomous Driving ◽  
Discrete Control ◽  
Driving Safety ◽  
Future Research ◽  
Continuous Control ◽  
Development Mode

Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the autonomy features in the Tesla Model S, recording my experiences over a 6-month period, including assessments of SA and problems with the autonomy. This preliminary analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. Issues were found with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction. New insights into challenges with semiautonomous driving systems include increased variability in SA, the replacement of continuous control with serial discrete control, and the need for more complex decisions. Issues that deserve consideration in future research and a set of guidelines for driver interfaces of autonomous systems are presented and used to create recommendations for improving driver SA when interacting with autonomous vehicles.

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

scholarly journals Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

2021 ◽  
Author(s):  
De Jong Yeong ◽  
Gustavo Velasco-Hernandez ◽  
John Barry ◽  
Joseph Walsh
Keyword(s):  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Future Research ◽  
Software Systems ◽  
Radar Sensors ◽  
Technical Performance ◽  
Driving Task ◽  
Single Sensor

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

scholarly journals Safety of Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jun Wang ◽  
Li Zhang ◽  
Yanjun Huang ◽  
Jian Zhao ◽  
Francesco Bella
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Statistical Investigation ◽  
The Other ◽  
Automated System ◽  
Future Research ◽  
Practical Applications ◽  
Safety Risks ◽  
Road Testing

Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.

scholarly journals Effective Techniques for Pedestrian Detection in Smart Autonomous Vehicles

Webology ◽  
2021 ◽  
Vol 18 (05) ◽  
pp. 1176-1183
Author(s):  
Thylashri S ◽  
Manikandaprabu N ◽  
Jayakumar T ◽  
Vijayachitra S ◽  
Kiruthiga G
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Pedestrian Detection ◽  
Autonomous Driving ◽  
The Elderly ◽  
Driving Safety ◽  
Research Trend ◽  
Automatic Identification ◽  
Smart Vehicles ◽  
Slowing Down

Pedestrians are essential objects in computer vision. Pedestrian detection in images or videos plays an important role in many applications such as real-time monitoring, counting pedestrians at various events, detecting falls of the elderly, etc. It is formulated as a problem of the automatic identification and location of pedestrians in pictures or videos. In real images, the art of pedestrian detection is an important task for major applications such as video surveillance, autonomous driving systems, etc. Pedestrian detection is also an important feature of the autonomous vehicle driving system because it identifies pedestrians and minimizes accidents between vehicles and pedestrians. The research trend in the field of vehicle electronics and driving safety, vision-based pedestrian recognition technologies for smart vehicles have established themselves loudly or slowing down the vehicle. In general, the visual pedestrian detection progression capable of be busted down into three consecutive steps: pedestrian detection, pedestrian recognition, and pedestrian tracking. There is also visual pedestrian recognition in the vehicle. Finally, we study the challenges and evolution of research in the future.

scholarly journals Evolution of V2X Communication and Integration of Blockchain for Security Enhancements

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1338 ◽  
Author(s):  
Rakesh Shrestha ◽  
Seung Yeob Nam ◽  
Rojeena Bajracharya ◽  
Shiho Kim
Keyword(s):  
Autonomous Vehicles ◽  
Vehicular Networks ◽  
High Reliability ◽  
Rapid Evolution ◽  
Edge Computing ◽  
Cloud Services ◽  
Driving Safety ◽  
Future Research ◽  
Traffic Efficiency ◽  
Multi Access

With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.

scholarly journals Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1339
Author(s):  
Miguel de Prado ◽  
Manuele Rusci ◽  
Alessandro Capotondi ◽  
Romain Donze ◽  
Luca Benini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Autonomous Driving ◽  
Ultra Low Power ◽  
Underlying Distribution ◽  
Target Environment ◽  
Vision Algorithm ◽  
Micro Controller Unit ◽  
Models At Runtime

Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime—trading accuracy and latency—which minimises the inference’s energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.

“Driver Take Over”: A Preliminary Exploration of Driver Trust and Performance in Autonomous Vehicles

2017 ◽  
Vol 61 (1) ◽  
pp. 1969-1973 ◽  
Author(s):  
Michelle Hester ◽  
Kevin Lee ◽  
Brian P. Dyre
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Preliminary Study ◽  
And Performance ◽  
Task Irrelevant ◽  
Statistical Results ◽  
And Task

Automated vehicles are becoming more prominent in research and development. These automated vehicles introduce issues that have been seen in other autonomous systems such as decreases in situation awareness, complacency, and trust. Previous literature has looked at the effects of alerts and voice agents on driving performance. This preliminary study compares different in-car alerts (no alert, sound alert, task irrelevant voice alert, and task relevant voice alert) on trust and the driver’s ability to get back in-the-loop when the automation has failed. Participants were asked to monitor a simulated automated vehicle as it drove down a straight two-lane road. The main statistical results of our study show no difference in trust between the four different conditions; however, more participants avoided collision with a leading car in the task relevant voice condition in comparison to the three other conditions. These preliminary findings have important implications for the design of automated vehicles.

From Here to Autonomy

2016 ◽  
Vol 59 (1) ◽  
pp. 5-27 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Human Performance ◽  
Autonomous Systems ◽  
Future Research ◽  
Adaptive Automation ◽  
Out Of The Loop ◽  
Human Autonomy ◽  
Levels Of Automation ◽  
Human Operators ◽  
Reliability And Robustness

As autonomous and semiautonomous systems are developed for automotive, aviation, cyber, robotics and other applications, the ability of human operators to effectively oversee and interact with them when needed poses a significant challenge. An automation conundrum exists in which as more autonomy is added to a system, and its reliability and robustness increase, the lower the situation awareness of human operators and the less likely that they will be able to take over manual control when needed. The human–autonomy systems oversight model integrates several decades of relevant autonomy research on operator situation awareness, out-of-the-loop performance problems, monitoring, and trust, which are all major challenges underlying the automation conundrum. Key design interventions for improving human performance in interacting with autonomous systems are integrated in the model, including human–automation interface features and central automation interaction paradigms comprising levels of automation, adaptive automation, and granularity of control approaches. Recommendations for the design of human–autonomy interfaces are presented and directions for future research discussed.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

scholarly journals Information Management Challenges in Autonomous Vehicles

2021 ◽  
Vol 23 (3) ◽  
pp. 58-77
Author(s):  
Adrija Ghansiyal ◽  
Mamta Mittal ◽  
Arpan Kumar Kar
Keyword(s):  
Information Management ◽  
Automobile Industry ◽  
Technology Implementation ◽  
Autonomous Vehicles ◽  
Autonomous Robots ◽  
Autonomous Systems ◽  
Future Research ◽  
Internet Of Vehicles ◽  
Connected Cars ◽  
Internet Of Things Technology

The focus of automobile industry is towards producing efficient driverless cars that are risk free and with zero tolerance to safety violations thereby following in the footsteps of autonomous robots. In this study, the author elaborates on the vulnerabilities relevant to internet of things technology implementation in these connected cars, commonly termed as internet of vehicles. This topic has already been discussed frequently by the research community; however, the main contribution of the paper is to establish the connection of information management with autonomous systems, an aspect that other literatures lack. The focus of the study is on presenting a brief introduction to the foundation technologies used in the connected vehicles. It also aims to summarize the various security methods that have been used infrequently and could be further explored in future research.

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