Cybersecurity Challenges In The Uptake Of Artifitial Intelligence in Autonomous Driving [1]

“Cybersecurity Challenges In The Uptake Of Artifitial Intelligence in Autonomous Driving”是ENISA发布的关于自动驾驶汽车中，由于AI技术的大量应用所带来的网络安全问题的技术白皮书。

全文可以分为三大部分：第一部分是对自动驾驶汽车的软硬件，及相关AI技术的系统性介绍；第二部分讲述AI技术在自动驾驶场景下的网络安全风险；第三部分则给出了相应的应对措施建议。

1. Introduction

The main contributions of this report are summarized below:
• State-of-the-art literature survey on AI in the context of AVs.
• Mapping of AVs’ functions to their respective AI techniques.
• Analysis of cybersecurity vulnerabilities of AI in the context of autonomous driving.
• Presentation and illustration (theoretical and experimental) of possible attack scenarios against the AI components of vehicles.
• Presentation of challenges and corresponding recommendations to enhance security of AI in autonomous driving

2. AI Techniques in automotive functions

2.1和2.2章节是关于自动驾驶系统的科普知识，涵盖了软硬件系统的简介

2.1 AI in autonomous vehicle

The last decade has seen an increase of efforts towards the development of AVs. An AV is a driving system that observes and understands its environment, makes decisions to safely, smoothly reach a desired location, and takes actions based on these decisions to control the vehicle. A key enabler of this race towards fully AVs are the recent advances in Al, and in particular in ML . Designing an AV is a challenging problem that requires tackling a wide range of environmental conditions (lightning, weather, etc.) and multiple complex tasks such as:

• Road following
• Obstacle avoidance
• Abiding with the legislation
• Smooth driving style
• Manoeuvre coordination with other elements of the ecosystem (e.g. vehicles, scooters, bikes, pedestrians, etc.)
• Control of the commands of the vehicle

Usually, autonomous driving is described as a sequential perception-planning-control pipeline, each of the stages being designed to solve one specific group of tasks [50]. The pipeline considers input data, generally from sensors, land returns commands to the actuators of the vehicle. The main components of a driving-assistant as well as of an AV are broadly grouped into hardware and software components. The hardware component includes sensors, V2X facilities, and actuators for control. The software part comprises methods to implement the vehicle perception, planning, decision and control capability. Figure 3 displays typical elements of this pipeline. They are implemented by decomposing each problem into smaller tasks, and developing independent models, usually using ML , for each of these tasks.

This chapter is structured as follows: First, a brief introduction to the main high-level automotive functions where Al plays an important role is given, as well as a presentation of the main hardware sensors that can be found on vehicles, and that generate the data that are processed by Al software components. After these two sections, a description of the main Al techniques commonly used is given, followed by a discussion on how these techniques are leveraged to implement the high-level functions in AVs. Finally, a summary of the chapter is presented in the form of three tables, highlighting the links between functions, hardware and software components, and techniques.

2.1.1 High-level automotive functions

Currently, fully autonomous driving solutions are being mostly experimented with prototypes. Nonetheless, vehicles with levels of automation up to level 3 are already on the road, with driving assistance functionalities relying on Al and ML. Technology-enhanced functionalities featured by commercialised vehicles that leverage the use of Al and ML are, for instance, braking assistance, smart parking, or vocal interactions with the infotainment system.

Features of AVs can be decomposed into several high-level automotive functions that are typically used by car manufacturers to advertise the autonomous capabilities of their products. As of today, the technical specifications of such functions are not uniformly defined and vary between manufacturers. In the following, we provide a non-exhaustive list of the most common automotive functions that are deemed as essential to achieve autonomous driving [10]. It is worth noting that most functions have been primarily designed to assist drivers rather than replace them (in vehicles with a level of autonomy from 1 to 3), by providing warnings, or taking control of the vehicles in limited situations. With fully developed AVs, these functions are part of the driving process and, essentially, contribute to replacing the driver1. At the end of this chapter, the following functions are considered and are mapped to specific Al tasks:

• Adaptive cruise control (ACC) consists in adjusting the speed of the vehicle in order to maintain an optimal distance from vehicles ahead. ACC estimates the distance between vehicles and accelerate or decelerate to preserve the right distance [51].
• Automatic Parking (or parking assistance) systems consist in moving the vehicle from a traffic lane into a car park. This includes taking into account the markings on the road, the surroundings vehicles, and the space available, and generate a sequence of commands to perform the manoeuvre [52].
• Automotive navigation consists in finding directions to reach the desired destination, using position data provided by GNSS devices and the position of the vehicle in the perceived environment [53].
• Blind spot / cross traffic / lane change assistance consists in the detection of vehicles and pedestrians located on the side, behind and in front of the vehicle, e.g. when the vehicle turns in an intersection or when it changes lanes. Detection is usually performed using sensors located in different points of the car [54], [55].
• Collision avoidance (or forward collision warning) systems, consist in detecting potential forward collisions, and monitoring the speed to avoid them. These systems typically estimate the location and the speed of forward vehicles, pedestrians, or objects blocking a road, and react proactively to situations where a collision might happen.
• Automated lane keeping systems (ALKS) consist in keeping the vehicle centred in its traffic lane, through steering. This includes the detection of lane markings, the estimation of the trajectory of the lane in possible challenging conditions, and the generation of actions to steer the vehicle [56].
• Traffic sign recognition consists in recognizing the traffic signs put on the road and more generally all traffic markings giving driving instructions, such as traffic lights, road markings or signs. This implies to detect from camera sensors various indicators based on shape, colours, symbols, and texts [57].
• Environmental sound detection: consists in the detection and interpretation of environmental sounds that are relevant in a driving context, such as hom honking or sirens. This requires performing sound event detection in noisy situations.

In what follows, we first analyse the standard blocks of hardware and sensor components. We then give a brief overview over the most important Al techniques and their software realization used for designing AVs. The chapter concludes by mapping automotive functions to Al functions in order to facilitate the identification of relevant vulnerabilities and cybersecurity threats in autonomous driving. By narrowing down the Al techniques that are actually used in AVs, one scopes down the problem of identifying pertinent cybersecurity threats related to the use of Al in autonomous driving.

2.2 Hardware and sensors

Humans drive cars by taking actions with hands and feet, based on decisions made considering the input received from our senses, mainly sight and hearing. Similarly, AVs rely on a variety of sensors to observe the surroundings and provide data to the Al systems of the vehicle, and on actuators to control the motion of the vehicle. The hardware components allow the vehicle to sense the outside surroundings as well as the inside environment via specific sensors, to act via the actuators that regulate the car movement, and to communicate with other agents/devices via the V2X technology.

Sensors, as the primary source of information for Al systems, are a critical element of AVs. All sensors can be broadly classified in three distinct groups [S8]:

• Exteroceptive sensors are those sensors that are designed to perceive the environment that surrounds the vehicle. They are relatively new sensors present in cars, and are the eyes and ears of the car. Cameras and Light Detection and Ranging system (LIDARs) are the main vectors of information for driving purposes. Other sensors, such as Global Navigation Satellite Systems (GNSSs), Inertial Measurement Unit (IMU), radars and ultrasonic sensors, are also used to probe the environment, but tend to be limited to specific tasks (e.g. close detections, sound listening) or to add redundancy, increasing the reliability of results in the case of malfunction of a sensor.
• Proprioceptive sensors, on the other hand, are those that take measurements within the vehicle itself. They have been present in cars for decades, and are mostly used for control purposes. They include the set of analogue measurements that are encoded in digital form indicating values such as the engine’s revolution per minute (RPM), speed of the car (as measured by wheel’s rotation), direction of steering wheel, etc
• Other sensors are those sending the information that the vehicle might receive from its digital communication with other vehicles, V2V communications or V2I. They mainly concern the connected infrastructure of vehicles, and therefore they will not be discussed in the rest of the report

The integration of sensors in vehicles varies according to carmakers [59], [60] and depends on the software strategy chosen to process the streams of data. Very often, the inputs from multiple sensors are combined in a process called data fusion [61] to align all data streams before processing, as sensors are usually providing images from different natures (2D images, 3D point clouds, etc.) with different temporal and spatial resolution.

Table 1 presents the main characteristics of the most common sensors found on autonomous cars, in addition to the LIDARs and cameras. The localization of these sensors on the vehicle and their main uses are illustrated in Figure 3 .

2.2.1 LIDARs and cameras for computer vision

Cameras and LiDARs are the most widespread sensors in autonomous cars, used to reproduce and enhance human vision. Digital video cameras are able to obtain a 2D representation of the 3-dimensional world. They provide a stream (video feed, as a sequence of images) of 2D maps of points (pixels) encoding colour information. Computer stereo vision techniques can be applied using multiple cameras and/or considering the different images in relation to the known movement of the vehicle. Examples of images from cameras are depicted in Figure 4 .

A LiDAR illuminates the environments with lasers and collects the reflected light. The analysis of the signal received allows the generation of a depth map of the scene (see Figure 4). The depth map is further processed to recreate 3D maps of the environment [54] considering missing values in the acquired 3D data points, unexpected reflections due to wrong perception of surfaces, and many other issues that may appear during the acquisition in real world scenarios.

Compared to LiDARs, cameras have the advantage that they distinguish colours, allowing the recognition of elements such as road signs, traffic lights, vehicle lights or text wamings. However, cameras also exhibit certain limitations compared to LiDAR: camera vision could be impaired by certain weather conditions such as rain, fog or sudden light changes such as when a vehicle gets out of a tunnel, while these conditions would affect to a lesser degree a LiDAR system.

2.3 Al techniques

Al is generally defined as a collection of methods capable of rational and autonomous reasoning, action or decision making, adaptation to complex environments and/or to previously unseen circumstances [65]. Al was initially born as an academic discipline in the second half of the twentieth century and led since then to significant advances in the automation of some human level tasks, nonetheless without much impact beyond academic circles for a long time [66]. It is deeply rooted in the fields of computer science, discrete mathematics and statistics, with an eventful history before gaining the popularity that makes it nowadays a key domain of the current digital revolution, thanks to the tremendous performances achieved by modem systems.

Typical problems related to Al require the development of programs able to demonstrate some forms of reasoning, knowledge representation, planning, learning, and, more generally, cognitive capabilities.

These