This article deals with the specific context of an autonomous car navigating in an urban center within a shared space between pedestrians and cars. The driver delegates the control to the autonomous system while remaining seated in the driver's seat. The proposed study aims at giving a first insight into the definition of human perception of space applied to vehicles by testing the existence of a personal space around the car.It aims at measuring proxemic information about the driver's comfort zone in such conditions.Proxemics, or human perception of space, has been largely explored when applied to humans or to robots, leading to the concept of personal space, but poorly when applied to vehicles. In this article, we highlight the existence and the characteristics of a zone of comfort around the car which is not correlated to the risk of a collision between the car and other road users. Our experiment includes 19 volunteers using a virtual reality headset to look at 30 scenarios filmed in 360{\textdegree} from the point of view of a passenger sitting in the driver's seat of an autonomous car.They were asked to say "stop" when they felt discomfort visualizing the scenarios.As said, the scenarios voluntarily avoid collision effect as we do not want to measure fear but discomfort.The scenarios involve one or three pedestrians walking past the car at different distances from the wings of the car, relative to the direction of motion of the car, on both sides. The car is either static or moving straight forward at different speeds.The results indicate the existence of a comfort zone around the car in which intrusion causes discomfort.The size of the comfort zone is sensitive neither to the side of the car where the pedestrian passes nor to the number of pedestrians. In contrast, the feeling of discomfort is relative to the car's motion (static or moving).Another outcome from this study is an illustration of the usage of first person 360{\textdegree} video and a virtual reality headset to evaluate feelings of a passenger within an autonomous car.
Deep reinforcement learning is widely used to train autonomous cars in a simulated environment. Still, autonomous cars are well known for being vulnerable when exposed to adversarial attacks. This raises the question of whether we can train the adversary as a driving agent for finding failure scenarios in autonomous cars, and then retrain autonomous cars with new adversarial inputs to improve their robustness. In this work, we first train and compare adversarial car policy on two custom reward functions to test the driving control decision of autonomous cars in a multi-agent setting. Second, we verify that adversarial examples can be used not only for finding unwanted autonomous driving behavior, but also for helping autonomous driving cars in improving their deep reinforcement learning policies. By using a high fidelity urban driving simulation environment and vision-based driving agents, we demonstrate that the autonomous cars retrained using the adversary player noticeably increase the performance of their driving policies in terms of reducing collision and offroad steering errors.
The discussion between the automotive industry, governments, ethicists, policy makers and general public about autonomous cars' moral agency is widening, and therefore we see the need to bring more insight into what meta-factors might actually influence the outcomes of such discussions, surveys and plebiscites. In our study, we focus on the psychological (personality traits), practical (active driving experience), gender and rhetoric/framing factors that might impact and even determine respondents' a priori preferences of autonomous cars' operation. We conducted an online survey (N=430) to collect data that show that the third person scenario is less biased than the first person scenario when presenting ethical dilemma related to autonomous cars. According to our analysis, gender bias should be explored in more extensive future studies as well. We recommend any participatory technology assessment discourse to use the third person scenario and to direct attention to the way any autonomous car related debate is introduced, especially in terms of linguistic and communication aspects and gender.
Development of the intelligent autonomous robot technology presupposes its anticipated beneficial effect on the individuals and societies. In the case of such disruptive emergent technology, not only questions of how to build, but also why to build and with what consequences are important. The field of ethics of intelligent autonomous robotic cars is a good example of research with actionable practical value, where a variety of stakeholders, including the legal system and other societal and governmental actors, as well as companies and businesses, collaborate bringing about shared view of ethics and societal aspects of technology. It could be used as a starting platform for the approaches to the development of intelligent autonomous robots in general, considering human-machine interfaces in different phases of the life cycle of technology - the development, implementation, testing, use and disposal. Drawing from our work on ethics of autonomous intelligent robocars, and the existing literature on ethics of robotics, our contribution consists of a set of values and ethical principles with identified challenges and proposed approaches for meeting them. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Our recommendations of ethical requirements for autonomous cars can be used for other types of intelligent autonomous robots, with the caveat for social robots that require more research regarding interactions with the users. We emphasize that existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need of a continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders.
Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior exposes the incentives we provide in its cost function. In this work, we argue for cars that are not optimizing a purely selfish cost, but also try to be courteous to other interactive drivers. We formalize courtesy as a term in the objective that measures the increase in another driver's cost induced by the autonomous car's behavior. Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly. We analyze the effect of courtesy in a variety of scenarios. We find, for example, that courteous robot cars leave more space when merging in front of a human driver. Moreover, we find that such a courtesy term can help explain real human driver behavior on the NGSIM dataset.
Deep reinforcement learning is actively used for training autonomous driving agents in a vision-based urban simulated environment. Due to the large availability of various reinforcement learning algorithms, we are still unsure of which one works better while training autonomous cars in single-agent as well as multi-agent driving environments. A comparison of deep reinforcement learning in vision-based autonomous driving will open up the possibilities for training better autonomous car policies. Also, autonomous cars trained on deep reinforcement learning-based algorithms are known for being vulnerable to adversarial attacks, and we have less information on which algorithms would act as a good adversarial agent. In this work, we provide a systematic evaluation and comparative analysis of 6 deep reinforcement learning algorithms for autonomous and adversarial driving in four-way intersection scenario. Specifically, we first train autonomous cars using state-of-the-art deep reinforcement learning algorithms. Second, we test driving capabilities of the trained autonomous policies in single-agent as well as multi-agent scenarios. Lastly, we use the same deep reinforcement learning algorithms to train adversarial driving agents, in order to test the driving performance of autonomous cars and look for possible collision and offroad driving scenarios. We perform experiments by using vision-only high fidelity urban driving simulated environments.
For a long time, autonomous cars were found only in science fiction movies and series but now they are becoming a reality. The opportunity to pick such a vehicle at your garage forecourt is closer than you think. As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures, such as OpenMP for multi-threading CPUs and OpenCL for GPUs. With the use of tiny but full-feature embedded supercomputers, computer stereo vision has been prevalently applied in autonomous cars for depth perception. The two key aspects of computer stereo vision are speed and accuracy. They are two desirable but conflicting properties -- the algorithms with better disparity accuracy usually have higher computational complexity. Therefore, the main aim of developing a computer stereo vision algorithm for resource-limited hardware is to improve the trade-off between speed and accuracy. In this chapter, we first introduce the autonomous car system, from the hardware aspect to the software aspect. We then discuss four autonomous car perception functionalities, including: 1) visual feature detection, description and matching, 2) 3D information acquisition, 3) object detection/recognition and 4) semantic image segmentation. Finally, we introduce the principles of computer stereo vision and parallel computing.
Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads. However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases. In this paper, we design, implement and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explores different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.
Sign recognition is an integral part of autonomous cars. Any misclassification of traffic signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to even a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS). In particular, we introduce two novel methods to create these toxic signs. First, we propose Out-of-Distribution attacks, which expand the scope of adversarial examples by enabling the adversary to generate these starting from an arbitrary point in the image space compared to prior attacks which are restricted to existing training/test data (In-Distribution). Second, we present the Lenticular Printing attack, which relies on an optical phenomenon to deceive the traffic sign recognition system. We extensively evaluate the effectiveness of the proposed attacks in both virtual and real-world settings and consider both white-box and black-box threat models. Our results demonstrate that the proposed attacks are successful under both settings and threat models. We further show that Out-of-Distribution attacks can outperform In-Distribution attacks on classifiers defended using the adversarial training defense, exposing a new attack vector for these defenses.
With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want aggressive cars, defensive drivers want defensive cars. In this paper, we put that assumption to the test. We find that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their actual driving style tends to be more aggressive. We also find that preferences do depend on the specific driving scenario, opening the door for new ways of learning driving style preference.
Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.
Security is of primary importance to vehicles. The viability of performing remote intrusions onto the in-vehicle network has been manifested. In regard to unmanned autonomous cars, limited work has been done to detect intrusions for them while existing intrusion detection systems (IDSs) embrace limitations against strong adversaries. In this paper, we consider the very nature of autonomous car and leverage the road context to build a novel IDS, named Road context-aware IDS (RAIDS). When a computer-controlled car is driving through continuous roads, road contexts and genuine frames transmitted on the car's in-vehicle network should resemble a regular and intelligible pattern. RAIDS hence employs a lightweight machine learning model to extract road contexts from sensory information (e.g., camera images and distance sensor values) that are used to generate control signals for maneuvering the car. With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network. Anomalous frames that substantially deviate from road context will be discerned as intrusions. We have implemented a prototype of RAIDS with neural networks, and conducted experiments on a Raspberry Pi with extensive datasets and meaningful intrusion cases. Evaluations show that RAIDS significantly outperforms state-of-the-art IDS without using road context by up to 99.9% accuracy and short response time.
Two current methods used to train autonomous cars are reinforcement learning and imitation learning. This research develops a new learning methodology and systematic approach in both a simulated and a smaller real world environment by integrating supervised imitation learning into reinforcement learning to make the RL training data collection process more effective and efficient. By combining the two methods, the proposed research successfully leverages the advantages of both RL and IL methods. First, a real mini-scale robot car was assembled and trained on a 6 feet by 9 feet real world track using imitation learning. During the process, a handle controller was used to control the mini-scale robot car to drive on the track by imitating a human expert driver and manually recorded the actions using Microsoft AirSim's API. 331 accurate human-like reward training samples were able to be generated and collected. Then, an agent was trained in the Microsoft AirSim simulator using reinforcement learning for 6 hours with the initial 331 reward data inputted from imitation learning training. After a 6-hour training period, the mini-scale robot car was able to successfully drive full laps around the 6 feet by 9 feet track autonomously while the mini-scale robot car was unable to complete one full lap round the track even after 30 hour training pure RL training. With 80% less training time, the new methodology produced significantly more average rewards per hour. Thus, the new methodology was able to save a significant amount of training time and can be used to accelerate the adoption of RL in autonomous driving, which would help produce more efficient and better results in the long run when applied to real life scenarios. Key Words: Reinforcement Learning (RL), Imitation Learning (IL), Autonomous Driving, Human Driving Data, CNN
This paper proposes a mechanism to assess the safety of autonomous cars. It assesses the car's safety in scenarios where the car must avoid collision with an adversary. Core to this mechanism is a safety measure, called Safe-Kamikaze Distance (SKD), which computes the average similarity between sets of safe adversary's trajectories and kamikaze trajectories close to the safe trajectories. The kamikaze trajectories are generated based on planning under uncertainty techniques, namely the Partially Observable Markov Decision Processes, to account for the partially observed car policy from the point of view of the adversary. We found that SKD is inversely proportional to the upper bound on the probability that a small deformation changes a collision-free trajectory of the adversary into a colliding one. We perform systematic tests on a scenario where the adversary is a pedestrian crossing a single-lane road in front of the car being assessed --which is, one of the scenarios in the Euro-NCAP's Vulnerable Road User (VRU) tests on Autonomous Emergency Braking. Simulation results on assessing cars with basic controllers and a test on a Machine-Learning controller using a high-fidelity simulator indicates promising results for SKD to measure the safety of autonomous cars. Moreover, the time taken for each simulation test is under 11 seconds, enabling a sufficient statistics to compute SKD from simulation to be generated on a quad-core desktop in less than 25 minutes.
Road congestion induces significant costs across the world, and road network disturbances, such as traffic accidents, can cause highly congested traffic patterns. If a planner had control over the routing of all vehicles in the network, they could easily reverse this effect. In a more realistic scenario, we consider a planner that controls autonomous cars, which are a fraction of all present cars. We study a dynamic routing game, in which the route choices of autonomous cars can be controlled and the human drivers react selfishly and dynamically to autonomous cars' actions. As the problem is prohibitively large, we use deep reinforcement learning to learn a policy for controlling the autonomous vehicles. This policy influences human drivers to route themselves in such a way that minimizes congestion on the network. To gauge the effectiveness of our learned policies, we establish theoretical results characterizing equilibria on a network of parallel roads and empirically compare the learned policy results with best possible equilibria. Moreover, we show that in the absence of these policies, high demands and network perturbations would result in large congestion, whereas using the policy greatly decreases the travel times by minimizing the congestion. To the best of our knowledge, this is the first work that employs deep reinforcement learning to reduce congestion by influencing humans' routing decisions in mixed-autonomy traffic.
The control for aggressive driving of autonomous cars is challenging due to the presence of significant tyre slip. Data-driven and mechanism-based methods for the modeling and control of autonomous cars under aggressive driving conditions are limited in data efficiency and adaptability respectively. This paper is an attempt toward the fusion of the two classes of methods. By means of a modular design that is consisted of mechanism-based and data-driven components, and aware of the two-timescale phenomenon in the car model, our approach effectively improves over previous methods in terms of data efficiency, ability of transfer and final performance. The hybrid mechanism-and-data-driven approach is verified on TORCS (The Open Racing Car Simulator). Experiment results demonstrate the benefit of our approach over purely mechanism-based and purely data-driven methods.
The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.
The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles are deployed on urban roads a minimum safety level must be assured. Since the autonomous car technology is still under development there is no standard methodology to evaluate such systems. It is important to completely understand the technology that is being developed to design efficient means to evaluate it. In this paper we assume safety-critical systems reliability as a safety measure. We model an autonomous road vehicle as an intelligent agent and we approach its evaluation from an artificial intelligence perspective. Our focus is the evaluation of perception and decision making systems and also to propose a systematic method to evaluate their integration in the vehicle. We identify critical aspects of the data dependency from the artificial intelligence state of the art models and we also propose procedures to reproduce them.
Autonomous cars can perform poorly for many reasons. They may have perception issues, incorrect dynamics models, be unaware of obscure rules of human traffic systems, or follow certain rules too conservatively. Regardless of the exact failure mode of the car, often human drivers around the car are behaving correctly. For example, even if the car does not know that it should pull over when an ambulance races by, other humans on the road will know and will pull over. We propose to make socially cohesive cars that leverage the behavior of nearby human drivers to act in ways that are safer and more socially acceptable. The simple intuition behind our algorithm is that if all the humans are consistently behaving in a particular way, then the autonomous car probably should too. We analyze the performance of our algorithm in a variety of scenarios and conduct a user study to assess people's attitudes towards socially cohesive cars. We find that people are surprisingly tolerant of mistakes that cohesive cars might make in order to get the benefits of driving in a car with a safer, or even just more socially acceptable behavior.
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