# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 1 Introduction
A fully autonomous car, with the driver traveling on the highway without neither steering wheel nor pedals, and without the need to monitor the vehicle at all has become a dream technically feasible. Its social advantages are expected to be huge, centered in the removal of human error from driving, with an expected 90% decrease in fatalities, and with relevant improvements related to the reduction of traffic jams and fuel emission, while enabling access to mobility for the aging and disabled populations. New ownership models are expected to appear and several industries (from automotive repair to parking, not forgetting airlines and several others) are expected to be disrupted, with novel business models arising from a social change comparable only to the introduction of the mobile phone. Obviously, autonomous cars will be the first due to the market size they represent, but other autonomous vehicles on the ground, in the air and sea will progressively become partially or completely unmanned, from trains to vessels. This change of paradigm, however, needs reliable sensor suites able to completely monitor the environment of the vehicle, with systems based on different working principles and with different failure modes to anticipate all possible situations. A combination of radar, video cameras, and lidar combined with deep learning procedures is the most likely solution for a vast majority of cases [15]. Lidar, thus, will be at the core of this revolution. However, the rush towards the autonomous car and robotic vehicles has forced the requirements of lidar sensors into new directions from those of remote sensing. Lidar imaging systems for automotive require a combination of long-range, high spatial resolution, real-time performance and tolerance to solar background in the daytime, which has pushed the technology to its limits. Different specifications with different working principles have appeared for different possible usage cases, including short and long-range, or narrow and wide fields of view. Rotating lidar imagers were the first to achieve the required performances, using a rotating wheel configuration at high speed and multiple stackeddetectors [16]. However, large-scale automotive applications required additional performance, like the capability to industrialize the sensor to achieve reliability and ease of manufacturing in order to get a final low-cost unit; or to have a small, nicely packaged sensor fitting in small volumes of the car. It was soon obvious that different lidar sensors were required to cover all the needs of the future self-driving car, e.g., to cover short and long-range 3D imaging with different needs regarding fields of view. Further, the uses of such a sensor in other markets, such as robotics or defense applications, has raised a quest for the final solid-state lidar, in which different competing approaches and systems have been proposed, a new proposal of set-up appears frequently, and a patent family even more frequently. This paper intends to neutrally introduce the basic aspects of lidar imaging systems applied to autonomous vehicles, especially regarding automobiles, which is the largest and fastest developing market. Due to the strong activity in the field, our goal has been more to focus on the basic details of the techniques and components currently being used, rather than to propose a technical comparison of the different solutions involved. However, we will comment on the main advantages and disadvantages of each approach, and try to provide further bibliography on each aspect for interested readers. Furthermore, an effort has been done in order to skip mentioning the technology used by each manufacturer as this hardly could be complete, would be based on assumptions in some cases and could be subject to fast changes. To those interested, there are excellent reports which identify the technology used for each manufacturer at the moment when they were written [1]. The remaining of the paper has been organized as follows.