Thao Nguyen, MJLST Staffer
Traffic accidents are a major issue in the U.S. and around the world. Although car safety features are continuously enhanced and improved, traffic crashes continue to be the leading cause of non-natural death for U.S. citizens. Most of the time, the primary causes are human errors rather than instrumental failures. Therefore, autonomous vehicles (“AVs”), which promise to be the automobiles that operate themselves without the human driver, are an exciting up and coming technology, studied and developed in both academia and industry.
To drive themselves, AVs must be able to perform two key tasks: sensing the surrounding environment and “driving”—essentially replacing the eyes and hands of the human driver. The standard AV design today includes a sensing system that collects information from the outside world, assisting the “driving” function. The sensing system is composed of a variety of sensors, most commonly a Light Detection and Ranging (LiDAR) and cameras. A LiDAR is a device that emits laser pulses and uses sound navigation and ranging (“SONAR”) principles to get a depth estimation of the surroundings: the emitted laser pulses travel forward, hit an object, then bounce back to the receivers; the time taken for the pulses to travel back is measured, and the distance is computed. With this information about distance and depth, a 3D point cloud map is generated about the surrounding environment. In addition to precise 3D coordinates, most LiDAR systems also record “intensity.” “Intensity” is the measure of the return strength of the laser pulse, which is based, in part, on the reflectivity of the surface struck by the laser pulse. LiDAR “intensity” data thus reveal helpful information about the surface characteristics of their surroundings. The two sensors, the camera and the LiDAR, complement each other: the former conveys rich appearance data with more details on the objects, whereas the latter is able to capture 3D measurements. Fusing the information acquired by each allows the sensing system to gain a reliable environmental perception.
LiDAR sensing technology is usually combined with artificial intelligence, as its goal is to imitate and eventually replace human perception in driving. Today, the majority of artificial intelligences use “machine learning,” a method that gives computers the ability to learn without explicitly being programmed. With machine learning, computers train itself to do new tasks in a similar manner as do humans: by exploring data, identifying patterns, and improving upon past experiences. Applied machine learning is data-driven: the greater the breadth and depth of the data supplied to the computer, the greater the variety and complexity of the tasks that the computer can program itself to do. Since “driving” is a combination of multiple high-complexity tasks, such as object detection, path planning, localization, lane detection, etc., an AV that drives itself requires voluminous data in order to operate properly and effectively.
“Big data” is already considered a valuable commodity in the modern world. In the case of AVs, however, this data will be of public streets and road users, and the large-scale collection of this data is empowered further by various technologies to detect and identify, track and trace, mine and profile data. When profiles about a person’s traffic movements and behaviors exist in a database somewhere, there is a great temptation for the information to be used for other purposes than the purpose for which they were originally collected, as has been the case with a lot of other “big data” today. Law enforcement officers who get their hands on these AVs data can track and monitor people’s whereabouts, pinpointing individuals whose trajectories touch on suspicious locations at a high frequency. The trajectories can be matched with the individual identified via use of car models and license plates. The police may then identify crime suspects based on being able to see the trajectories of everyone in the same town, rather than taking the trouble to identify and physically track each suspect. Can this use of data by law enforcement be sufficiently justified?
As we know, use of “helpful” police tools may be restricted by the Fourth Amendment, and for good reasons. Although surveillance helps police officers detect criminals, extraneous surveillance has its social costs: restricted privacy and a sense of being “watched” by the government inhibits citizens’ productivity, creativity, spontaneity, and causes other psychological effects. Case law has given us guidance to interpret and apply the Fourth Amendment standards of “trespass” or “unreasonable searches and seizures” by the police. Three principal cases, Olmstead v. United States, 277 U.S. 438 (1928), Goldman v. United States, 316 U.S. 129 (1942), and United States v. Jones, 565 U.S. 400 (2012), a modern case, limit Fourth Amendment protection to protecting against physical intrusion into private homes and properties. Such protection would not be helpful in the case of LiDAR, which operates on public street as a remote sensing technology. Nonetheless, despite the Jones case, the more broad “reasonable expectation of privacy” test established by Katz v. United States, 389 U.S. 347 (1967) is more widely accepted. Instead of tracing physical boundaries of “persons, houses, papers, and effects,” the Katz test focuses on whether there is an expectation of privacy that is socially recognized as “reasonable.” The Fourth Amendment “protects people, not places,” wrote the Katz court.
United States v. Knotts, 460 U.S. 276 (1983) was a public street surveillance case that invoked the Katz test. In Knotts, the police installed a beeper on to the defendant’s vehicle to track it. The Court found that such tracking on public streets was not prohibited by the Fourth Amendment: “A person traveling in an automobile on public thoroughfares has no reasonable expectation of privacy in his movements from one place to another.” The Knotts Court thus applied the Katz test and considered the question of whether there was a “reasonable expectation of privacy,” meaning that such expectation was recognized as “reasonable” by society. The Court’s answer is in the negative: unlike a person in his dwelling place, a person who is traveling on public streets “voluntarily conveyed to anyone who wanted to look at the fact that he was traveling over particular roads in a particular direction.”
United States v. Maynard, 615 F.3d 544 (2010), another public street surveillance case taking place in the twenty-first century, reconsidered the Knotts holding regarding “reasonable expectation of privacy” on public streets. The Maynard defendant argued that the district court erred in admitting evidence acquired by the police’s warrantless use of a Global Pointing System (GPS) device to track defendant’s movements continuously for a month. The Government invoked United States v. Knotts and its holding that “[a] person traveling in an automobile on public thoroughfares has no reasonable expectation of privacy in his movements from one place to another.” The DC Circuit Court of Appeals, however, distinguished Knotts, pointing out that the Government in Knotts used a beeper that tracked a single journey, whereas the Government’s GPS monitoring in Maynard was sustained 24 hours a day continuously for one month.The use of the GPS device over the course of one month did more than simply tracking defendant’s “movements from one place to another.” The result in Maynard was the discovery of the “totality and pattern” of defendant’s movement. The Court is willing to make a distinction between “one path” and “the totality of one’s movement”: since someone’s “totality of movement” is much less exposed to the view of the public and much more revealing of that person’s personal life, it is constitutional for the police to track an individual on “one path,” but not that same individual’s “totality of movement.”
Thus, with time the Supreme Court appears to be recognizing that when it comes to modern surveillance technology, the sheer quantity and the revealing nature of data collected on movements of public street users ought to raise concerns. The straightforward application of these to AV sensing data would be that data concerning a person’s “one path” can be obtained and used, but not the totality of a person’s movement. It is unclear where to draw the line between “one path” and “the totality of movement.” The surveillance in Knotts was intermittent over the course of three days, whereas the defendant in Maynard was tracked for over one month. The limit would perhaps fall somewhere in between.
Furthermore, this straightforward application is complicated by the fact that the sensors utilized by AVs do not pick up mere locational information. As discussed above, AV sensing system, being composed of multiple sensors, capture both camera images and information about speed, texture, and depth of the object. In other words, AVs do not merely track a vehicle’s location like a beeper or GPS, but they “see” the vehicle through their cameras and LiDAR and radar devices, gaining a wealth of information. This means that even if only data about “one path” of a person movement is extracted, this “one path” data as processed by AV sensing systems is much more in-depth than what a beeper or CSLI can communicate. Adding to this, current developers are proposing to create AVs networks that share data among many vehicles, so that data on “one path” can potentially be combined with other data of the same vehicle’s movement, or multiple views of the same “one path” from different perspectives can be combined. The extensiveness of these data goes far beyond the precedents in Knotts and Maynard. Thus, it is foreseeable that unwarranted subpoenaing AVs sensing data is firmly within the Supreme Court’s definition of a “trespass.”
 Tri Nguyen, Fusing LIDAR sensor and RGB camera for object detection in autonomous vehicle with fuzzy logic approach, 2021 International Conference on Information Networking (ICOIN) 788, 788 (2021).
 Id. (“An autonomous vehicle or self-driving car is a vehicle having the ability to sense the surrounding environment and capable of operation on its own without any human interference. The key to the perception system holding responsibility to collect the information in the outside world and determine the safety of the vehicle is a variety of sensors mounting on it.”)
 Id. “The key to the perception system holding responsibility to collect the information in the outside world and determine the safety of the vehicle is a variety of sensors mounted on it.”
 Heng Wang and Xiaodong Zhang, Real-time vehicle detection and tracking using 3D LiDAR, Asian Journal of Control 1, 1 (“Light Detection and Ranging (LiDAR) and cameras [6,8] are two kinds of commonly used sensors for obstacle detection.”)
 Id. (“Light Detection and Ranging (LiDAR) and cameras [6,8] are two kinds of commonly used sensors for obstacle detection.”) (“Conversely, LiDARs are able to produce 3D measurements and are not affected by the illumination of the environment [9,10].”).
 Nguyen, supra note 1, at 788 (“Due to the complementary of two sensors, it is necessary to gain a more reliable environment perception by fusing the information acquired from these two sensors.”).
 Raymond P. Siljander & Darin D. Fredrickson, Fundamentals of Physical Surveillance: A Guide for Uniformed and Plainclothes Personnel, Second Edition (2002) (abstract).
 Tamara Dinev et al., Internet Privacy Concerns and Beliefs About Government Surveillance – An Empirical Investigation, 17 Journal of Strategic Information Systems 214, 221 (2008) (“Surveillance has social costs (Rosen, 2000) and inhibiting effects on spontaneity, creativity, productivity, and other psychological effects.”).
 Katz v. United States, 389 U.S. 347, 351 (1967).
 United States v. Knotts, , 460 U.S. 276, 281 (1983) (“A person traveling in an automobile on public thoroughfares has no reasonable expectation of privacy in his movements from one place to another.”)
 Id. at 282.
 United States v. Maynard, 615 F.3d 544, 549 (2010).
 Id. at 557.
 Id. at 556.
 Id. at 558 “[O]nes’s movements 24 hours a day for 28 days as he moved among scores of places, thereby discovering the totality and pattern of his movements.”).
 Knotts at 276.