The left turn problem for self-driving cars has surprising implications

Self-driving car technology advances rapidly, but critics frequently point out that some hard problems remain. John Leonard, who headed MIT’s self-driving cars project at the DARPA 2007 urban challenge, eloquently describes various challenging situations including hand-waving police officers and left turns in heavy traffic.

The hand-waving police officer problem can be solved easily with a simple workaround: The car just detects the hand waving situation. It then dispatches a camera feed to a remote control center and asks a remote human operator for guidance (similar to Google’s patent 8996224).

The left turn problem is more interesting. Such situations occur more frequently and they do present significant challenges. Self-driving car prototypes have been known to wait for long intervals at intersections before they finally made the left turn Рheavily testing the patience of human drivers stuck behind them. The video by John Leonard clearly shows how hard it can be to make a left turn when traffic is heavy  in all directions and slots between cars coming from the left and the right are small and rare.

How do human drivers handle such situations? First they wait and observe the traffic patterns. If opportunities for left turns are rare they adjust their driving strategy. They may accelerate faster and will try to inch into a smaller slot than usual. Sometimes they will move slightly into the lane of cars coming from the left to signal that they are intent on making a turn and expect other cars to make room or they will try to find an intermediate spot between the various lanes and break the left turn down into one move towards this spot, come to a stop there and then into a second move from the the intermediate position into the the target lane. Leonard is right that programming such maneuvers into self-driving cars presents a major challenge.

But the problem is more fundamental. When we develop self-driving cars, we gain insights about the domain of driving and extend our knowledge not only about algorithms but also about human driving. To make a left turn, self driving cars have to analyze the traffic situation at the intersection. They are much better than humans at simultaneously identifying the traffic participants in all directions, to detect their speed, and they are quite good at anticipating their trajectories. Current driverless car prototypes also have no problems to decide on an appropriate path for the left turn. When a self-driving car hesitates at an intersection, the reason is not a problem with the algorithm but rather that the self-driving car finds that the safety margins for executing the turn are too small in the current situation: the risk is too high. Unfortunately, this problem can not be solved through better algorithms but only by increasing the level of acceptable risk! The risk of a left turn at an intersection is determined by the layout of the intersection, physics and the range of potential behavior of the other traffic participants, none of which can be changed by the self-driving car.

Left turns are indeed known to be risky. We may not think about it when we make a left turn, but accident statistics paint a very clear picture. An NHTSA study that analyzed crossing path crashes found that police-reported crashes involving left turns (913,000) are almost 10 times as frequent than police-reported crashes of right turns (99,000). If we consider that right and left turns are not equally distributed in normal driving (right turns occur more frequently but exact data are not available)  then the risk of a left turn may be between 10 and 20 times larger than the risk of a right turn. In 2013 crashes between a motorcycle and another vehicle making a left turn cost 922 lives; this amounted to nearly half (42%) of all fatalities due to crashes involving a motorcycle and another vehicle. Arbella insurance reported that in 2013 31% of its severe accident claims involved left turns. Thus human drivers have little reason to be proud of their left-turn capabilities.

As a consequence, UPS has largely eliminated left turns many years ago. Recently the route planning app Waze has rolled out a new feature that allows users to plan routes without left turns. These two examples show that self-driving cars do not even need the capability of making left turns in heavy traffic. It is possible to get along without such turns.

Thus the left turn problem for self-driving cars leads to the following three insights:

1) The left turn problem is not so much a problem of self-driving cars, it really is a problem of human drivers who take too many risks at left turns as we can see from the large number of left-turn accidents and from the risk analysis which self-driving cars carefully perform when making a left turn. Autonomous cars should never make left turns as readily and rapidly as human drivers. As human drivers we need to be more self-critical about our own capabilities and more ready to question our assumptions about driving instead of using our driving behavior as our implicit standard for self-driving cars.

2) We need to carefully consider the acceptable risk profiles for self driving vehicles. Risk profiles are not black and white; there are more alternatives than the high levels of risk that we take every day as human drivers without much thinking and the minimal risk strategies adopted by all current self-driving car prototypes. It would be unacceptable to let self-driving cars barge into dense traffic in the same way as we sometimes consider viable and mostly get away with. But it would be possible to reduce the time that a driverless car has to wait when turning or merging by allowing the car to increase the acceptable risk by a small amount if clearly defined conditions are met.In this area, much work and thinking is required. Expecting a self-driving car to minimize all conceivable risks and then operate as quickly as human drivers is a contradiction in itself. Instead of minimizing all risks, we need to seriously discuss what kind of small risks should be allowed in well defined situations.

3) We should not underestimate the creativity of the market to deal with the remaining problems of self-driving cars. Many of the frequently-cited problems of self-driving cars have practical workarounds that don’t require that much intelligence (triple right-turns instead of a left turn, remote assistance to deal with the hand gesture problem, limit the first self-driving taxis to snow-free regions for the snow problem etc.).

German railways to introduce autonomous long distance trains by 2023

The CEO of Germany’s railways, Ruediger Grube, does not want to fall behind the auto industry with autonomous mobility and has announced that Deutsche Bahn (German Railways) will operate on parts of the railway network with full autonomy “by 2021, 2022, or 2023″. Test are already underway on a part of the German railway network in Eastern Germany.

The technology for autonomous long distance trains differs greatly from the technology for autonomous metro-trains and subways which already operate in many cities of the world. In the latter case, most of the intelligence for autonomous driving is embedded into the railroad infrastructure and a centralized controller that is in constant communication with all trains; the trains themselves, in contrast have little intelligence; they don’t operate autonomously. This approach is not viable for long-distance networks because upgrading thousands of kilometers of the network with controllers and sensors would be much to costly. Therefore most of the intelligence has to be embedded within the locomotive. Fully autonomous long distance trains therefore need to be equipped with sensors and algorithms that are very similar to those used in self-driving cars.

The advantage of self-driving trains does not lie so much in cost reduction but in the ability to increase network capacity because trains can be operated with higher frequencies and at shorter distances. This also increase the flexibility of rail-based transportation solutions and makes new services possible. These capabilities are essential if railroads want to survive against the greatly intensifying competition from fully autonomous self-driving cars, trucks and buses.

German unions immediately criticized their plans. But they fail to understand that fully autonomous road-based transportation will provide an enormous challenge for the railroads. Deutsche Bahn is on the right track. They should do everything to accelerate their introduction of autonomous long distance trains.