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the system realizes that the driver should have began to decrease speed, in order to approach the stopped car at a low speed. At this point, the system shifts to the yellow status, and a warning can be granted to the driver, asking him to decrease speed. If, regardless of the warning, the driver does not decrease the vehicle’s speed, the situation will come to a point where the crash between both vehicles is unavoidable because the maximum braking deceleration is not sufficient to stop the vehicle before the crash. In this case, the system enters a red status, and takes control of the brake, decelerating the vehicle as much as it can, in order to mitigate the consequences of the accident.
Obviously, unlike inAD, in the field of ADAS, there is the additional problem of defining the thresholds for these system status. Also, there is the problem of defining which are the adequate actions for a given situation. Suppose the example described above: during the yellow status, should the vehicle warn the driver of an eminent danger situation. And, if so, how should the warning be conveyed to the driver. There are several possibilities, from a sound warning to a flash light in the instrument pannel, etc. Such a system must be very carefully devised, because there is the chance that the driver gets further distracted by the warning itself and, because of it, is unable to execute the necessary actions to avoid the red status, and therefore, the crash. It is in this area of research, the human vehicle interface, thatADASmost distinguishes itself fromAD. However, the point that we wanted to make is that the core technologies are the same for both fields. Imagine that anADAS system, that constantly monitors the driver’s actions, is so efficient that it always can devise the appropriate driving actions for all situations. In such a case, the replacement of the human driver by a fully autonomous system would be a very easy task.
Another question that often is raised in theADandADAScommunities is that anyADorADAS system must be fault free, before it can be applied to commercial vehicles. Ultimately, no system is completely fault free. Yet the general opinion is that those that could be installed on autonomous vehicles should have such properties. Obviously, commercial systems should have a very low failure probability, and special efforts should be endeavored to avoid faults as much as possible. But in our opinion, the fault probability threshold necessary to consider their application to commercial vehicles is not of zero failure probability, but rather a smaller failure probability than that of the average of human drivers. Of course that a death caused by a failure of an autonomous system would have a very large impact on the media and in the general public opinion and this is a concern to the researchers in the field. However, from a statistical standpoint, if autonomous systems would cause a smaller amount of accidents, they would in fact be saving lives.
Given all these considerations, the question is why have not the automotive manufacturers yet introduced autonomous driving systems in commercial vehicles. There are several factors that have delayed or even stalled the process. Some of them are listed in the following lines.
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Sensors
Grasping a complete or at least sufficient amount of data from the road scenario and from the agents involved requires a very large set of sensors. Recent AD robotic prototypes display a vast array of radars, LRF, sonars, monocular visible and infrared cameras, stereo cameras, 3D lasers, time of flight cameras, GPS, inertial measurement units and other sensors. In general, all these sensors have undergone significant advances in recent years, making them more effective, precise and robust.
This has certainly been a factor that delayed the development ofADcapabilities. One symptomatic example is the Velodyne LIDAR [Velodyne 2012]. The Velodyne is a 3D LRF that produces 1.3 million range measurements per second, all around the vehicle. Apart from the notable exceptions from theDARPAchallenges of 2004 and 2005 (although these competitions did not involve coping with urban traffic) and the VisLab group, the fact is that Velodyne seems to be a standard in current autonomous vehicles. It should be noticed that this sensor was developed relatively recently, in 2007.
Hardware
As discussed,ADapplications require that the vehicles are equipped with vast amounts of sensors.
As a consequence, the amount of data received is also very large. Also, because the road scenarios are highly dynamic environments theADsystems have real time demands. The conjunction of both these factors led to the necessity of having very powerful computers onboard the vehicles, and to the fact that, for many years, the available hardware capabilities were insufficient to comply with these demands.
Recognition Systems
In order tounderstand the traffic, autonomous systems employ several algorithms for recognizing the agents that move about the road scene. In the field of pattern recognition, many algorithms have been proposed throughout the years to perform visual detection of pedestrians, lane markings, other vehicles, traffic signals, etc. There have been significant advances, and the systems now have very high hit rates (number of detected entities over the total number of entities), sometimes of over 99%. The problem has been related with the number of false positives, that is, the number of falsely detected entities over the total number of detection attempts. By today’s standards, values under 1%
are considered very interesting. However, within the context ofAD, these performances could be still not sufficient. Suppose a pedestrian detection system that has a 0.1% false alarm rate. Whenever a pedestrian is detected, the system warns the driver of the danger associated with a nearby pedestrian.
With a false alarm rate of 0.1%, the system is expected to falsely detect a pedestrian every 1000 detection attempts. If we consider that the system is processing images streaming from a camera at 30Hz, this means that a false detection would occur every 35 seconds. No driver would buy a system that would wrongly warn him every 30 seconds. In conclusion, the current state of the art on visual
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recognition of the road entities, in particular the level of false alarms, is still orders of magnitude away from what would be necessary for their application to autonomous systems. These problems have been partially solved by the inclusion of additional sensors likeLRF, or the conditional display of the warning by monitoring the drivers gaze, for example, the system issues warnings only when a pedestrian is detected and the driver’s gaze is not aimed in that direction.
Legal Issues
One thing that has certainly hampered the release of commercial vehicles with AD capabilities is the fact that it is forbidden by law in the entire European Union and most of the United States. In June 2011, the United States state of Nevada passed a law permitting the operation of driverless cars in Nevada. Google has been lobbying for driverless car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for a self driven car in May 2012. The license was issued to a Toyota Prius modified with Google’s experimental driverless technology. In August 2012, the team announced that they have completed over 480000 km autonomous driving with an accident free record. As of September 2012, three states have passed laws permitting driverless cars: Nevada, Florida and California. In Europe, with the lobbying from the powerful automotive industries, it is very possible that several countries will follow.
Another problem that is difficult to handle is that of legal responsibility, in particular for dealing with insurance contracts. In the case of an accident caused by an AD system, who is juridically accountable: the human passenger (that was not driving), the vehicle owner, the system manufacturer, the car manufacturer. It is a delicate issue that still remains to be solved.