Tag Archives: traffic data

Toronto selects HERE for smart city and traffic mitigation initiative

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HERE location technology enables Toronto to analyze traffic patterns and further establish the City’s data-driven approach to improving transportation network performance

HERE, the global leader in mapping and location services, is providing the City of Toronto with real-time and historical traffic data to support the city’s efforts to reduce road congestion and improve transportation services.

“Toronto is one of the fastest growing cities in North America, and as the population grows so does our effort to create a smarter traffic management system. We are pleased to have HERE on board to help Toronto’s citizens get around the city quickly, efficiently and safely,” said Barbara Gray, General Manager, Transportation Services, at the City of Toronto.

With HERE’s solution, which includes an analytic and reporting tool provided by Iteris, a global leader in applied informatics for transportation, the City of Toronto is developing a new understanding of transportation issues including the impact on traffic of weather conditions, construction works and infrastructure changes. Based on these observations, the city’s Big Data Innovation Team can manage the traffic in real-time and in a smart manner for the benefit of all Torontonians.

HERE’s extensive coverage creates visibility to speeds on every single road including arterial roads in the city centre. Not only do arterial roads represent a huge chunk of the road network and are therefore key to a smart network management strategy, their analysis is also complex. Many arterial roads, for example, must accommodate a wide range of users, such as pedestrians, bicycles and cars, with different needs.

“We are excited to work with the City of Toronto to turn big data into smart insights that can help alleviate congestion, enable safe road networks and reduce pollution,” said Monali Shah, Director of Intelligent Transportation Solutions at HERE.

In North America and globally HERE serves as a vital partner to smart cities and the broader public sector, helping them make decisions that improve the quality of life for citizens. For example, HERE supplies traffic information to several Department of Transportations across the United States, including Alabama, California, Connecticut, Florida, Georgia, Louisiana, Maryland, Michigan, Missouri, New Jersey, Ohio and New York, and is also supporting intelligent transportation initiatives in Colorado, Iowa and Michigan.

Source: HERE

Amsterdam and TomTom join forces to create a smarter city

 

Amsterdam, 23 November 2016 – TomTom (TOM2) and the City of Amsterdam will collaborate on the development of traffic and travel concepts to improve traffic flow and parking in the Dutch capital. Together with the city of Amsterdam, TomTom will investigate new ways to measure traffic flow, understand parking behavior and enable city planners and inhabitants to make smarter traffic decisions.

Using the insights from TomTom’s Traffic data, the city government will now be able to make better decisions about accessibility and mobility throughout the city. As a result of the agreement, traffic measures, such as road closures in the city centre, will be monitored in more detail, leading to rapid intervention if changes occur in the traffic situation. The cooperation will enable TomTom to gain even more insights into the needs of a city in terms of mobility and to further develop products to help a city’s mobility in the smartest way possible.

Deputy Mayor Pieter Litjens: “This cooperation will make the city of Amsterdam smarter. That’s good news for the accessibility, traffic flow and air quality in the city. For example, if your navigation system sends you straight away to a free parking spot, it’ll save you countless kilometres of pointless driving around searching one. Thanks to TomTom’s insights, we will be able to look very specifically at the outcome of measures we take and see how effective they were. That way, we can continuously improve traffic and mobility throughout Amsterdam.”

“This agreement adds to our ambition of making smarter cities of the future a reality,” said Ralf-Peter Schäfer, VP Traffic and Travel at TomTom. “TomTom’s ability to advise local authorities as well as consumers makes it uniquely placed to create better mobility for the City of Amsterdam. Our real-time travel information enables rapid response on changing traffic conditions and historical travel information enables better planning as well as an improved traffic distribution by utilising the whole available infrastructure.”

Source: Tom Tom

 

Vehicle-to-X technology from Continental protects vulnerable road users

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  • V2X technology enables communication between vehicles and vulnerable road users
  • Future will see smartphones exchanging position and movement data with vehicles via short-range communication
  • Number of fatal traffic accidents involving cyclists and pedestrians falling slower than those involving car occupants

International automotive supplier Continental is constantly developing new technologies to improve the safety of vulnerable road users (VRU). Based on Vehicle-to-X (V2X) communication, vehicles will be able to communicate with these road users. Short-range communication (e.g. WLANp) makes it possible to exchange position data in order to avoid possible collisions or significantly reduce accident severity. “Protecting vulnerable road users such as pedestrians and cyclists is one of the greatest challenges on the road to accident-free driving,” explained Dr. Bernhard Klumpp, Head of the Passive Safety & Sensorics business unit at Continental. “Short-range communication can play a decisive role here, too, and brings us one step closer to our goal of zero traffic fatalities.”

Networking between vehicles, vulnerable road users and infrastructure causes more potential for more safetyDownload Image

 

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Short-range communication helps prevent accidents involving vulnerable road users

A vehicle fitted with V2X technology is able to communicate with VRUs that carry a smartphone or a special transponder. Modern vehicle-to-vehicle and vehicle-to-infrastructure communication is based on a standard for direct ad-hoc communication (WLANp). In future, it will be possible to incorporate a smartphone into this ad-hoc communication so that they are able to communicate with vehicles using V2X. Modern smartphones are already capable of WLAN communication. With a few changes to the communication chip, smartphones can be adapted to exchange V2X messages with vehicles via WLANp.

Vehicle-to-X technology enables exchange of position and movement data between vehicles and vulnerable road usersDownload Image

“It is particularly important to make sure that the high standard of data security and functional reliability of V2X technology is also implemented when extended to communication with smartphones,” explained Dr. Gunnar Jürgens, Head of Development in the Passive Safety & Sensorics business unit, and Managing Director of Continental Safety Engineering GmbH in Alzenau, Germany. The position and movement predictions of the VRU are transmitted anonymously to the vehicle using V2X messages. Incoming messages are authenticated and processed within less than 0.1 seconds. The higher-level control unit in the vehicle decides whether the driver needs to be warned or if an intervention in the vehicle dynamics is necessary. As the GPS-based localization of a pedestrian is not precise enough, the focus in subsequent designs is on enhanced concepts for relative localization and movement prediction. Data fusion with data from other on-board sensors will be applied before potentially engaging the brakes. This can decisively improve object recognition and classification.

One big advantage of the communication using short-range communication, with a range of 300 to 500 meters, is the very low latency time. This is essential for exchanging safety-relevant information, such as the vehicle position, dynamics and brake operation.

VRUs account for almost 50 percent of traffic fatalities

According to Germany’s Federal Statistics Office, around 50 percent of people killed in traffic accidents are vulnerable road users such as cyclists, motorcyclists and pedestrians. Between 2000 and 2012, the number of fatal accidents involving car occupants fell by 50 percent. In contrast, the same statistic for vulnerable road users fell by less than 30 percent over the same period, according to the 2014 annual report of the Organization for Economic Co-operation and Development (OECD). With V2X technology, more and more accidents can be prevented, especially those involving vulnerable road users.

Source: Continental

 

HERE unveils first lane-level traffic reports

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HERE has developed  a new algorithm to identify fast and slow-moving lanes on the same road, providing better information for drivers on road conditions ahead and the fastest route to use.Split-lane-1

How much traffic congestion is on the road ahead? The answer is often ‘it depends’. The traffic that’s trying to turn off at the next junction might be backed up bumper-to-bumper, while the cars that are going past it on the inner lane are whizzing along. Or maybe it’s the other way round.

Because it’s hard to identify the specific lanes that cars are travelling on from current GPS signals, it’s therefore tricky for traffic reporting systems to identify situations like the above. Instead, you get an average speed of both the backed-up traffic and the freely moving vehicles.

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That’s not ideal for route planning – and might lead you to avoid a more direct route that’s actually faster than the average road speed suggests – because the traffic conditions are different for each lane.

HERE took this chronic problem head on and created a novel solution: Split Lane Traffic Reporting at Junctions. SLT Reporting at Junctions is based on an innovative and proprietary new algorithm by HERE described in our latest white paper. The algorithm solves the fundamental problem of reporting two different traffic speeds on a single road segment.

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To create the white paper and service HERE scanned its maps for major junctions and highway splits around the world, where there might be heavily congested traffic in one lane, and freely moving traffic in another. It turned out that there are over 100,000 such highway junctions worldwide. Then we rated each of these junctions according to how frequently there were significant differences in the speeds for each lane.

The solution identifies when there are very different traffic speeds being recorded around these junctions. It decides whether each of these probes belongs to the backed-up lane or the faster-moving lane, splitting the recorded speed for the road accordingly. SLT reports can show three speed profiles: free-flowing traffic, slow moving lanes, and congestion.

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“For the first time Split Lane Traffic Reporting creates the information necessary for a driver to know the road conditions in front of them down to the lane level. This is a solution tailored for all of us who’ve had to play the game of guessing which lane is the fastest to be in while stuck in traffic at a highway junction,” said Tony Belkin, Director of Traffic and Dynamic Content for HERE. “Split Lane Traffic Reporting at Junctions is the latest example of the innovative, quality driven and solutions based approach we take at HERE.”

Split Lane Traffic Reporting information will be automatically fed into the HEREReal Time Traffic service to provide better intelligence for drivers and for our routing software. Split Lane Traffic Reporting serves as an important example of the first-to-market products HERE delivers in support of not only today’s driver setting, but the increasing use of highly automated driving assistance systems.

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As more cars are able to take advantage of this information, and are guided byhighly-automated systems, we anticipate that overall traffic congestion itself will be reduced, together with the costs, wasted time, extra emissions and frustration that comes with these situations.

Source: Here

Image credit: Chatchai Kritsetsakul

Japan: In-Car driver monitoring used to detect traffic congestion

 

Toshio Ito, professor at the College of Systems Engineering and Science, Shibaura Institute of Technology, has developed a method to predict a traffic jam by observing the operations made by the driver.

Unlike the current method, which installs cameras, etc on roads, the new method realizes the prediction by using only existing in-vehicle sensors. Therefore, it can lower costs and does not depend on infrastructure. He has not yet decided when to commercialize the new technology.

A traffic jam occurs when a vehicle is decelerated by applying the brakes, etc on a busy road and following vehicles begin to decelerate after that. Based on this mechanism, Ito considered that it is possible to predict a traffic jam by detecting a state in which “the speeds of vehicles have not yet been reduced but the volume of traffic is increasing and a traffic jam seems to occur” by detecting changes in the driver’s behaviors.

As behaviors that the driver unconsciously changes, Ito cited “steering angle,” “how much the gas pedal is pressed down” and “vehicle speed.” All of those three factors can be detected by existing in-vehicle sensors.

Ito tried to detect the state that shows a sign of a traffic jam by analyzing those data. In an experiment using a driving simulator, he found a clear difference between the driver’s behaviors on a light-traffic road and those on a road where a traffic jam seems to occur, he said.

However, the changes of behaviors differed from driver to driver. A research group led by Ito invented a more accurate judging method. In the experiment, he found that the variance of the frequency components of data clearly differs depending on the state of a road.

Therefore, Ito optimized the system by using a machine learning technique called “neural network system” (NNS) to process the variance and classify data patterns. As a result, it became possible to detect differences in the state of a road based on the observation of the driver’s behaviors with an accuracy of 80%, he said.

Ito concluded that it is possible to predict a traffic jam by observing the data including the three factors. Because the new system can be realized just by developing software for the analysis of the driver’s behaviors, it can be introduced at a low cost without infrastructure on roads or additional in-vehicle sensors. Currently, patents for the new technology are pending.

The new method, which observes the driver’s behaviors, can be used for detecting not only a sign of a traffic jam but also changes in driving behaviors caused by physical and psychological conditions. Therefore, it can be used for notifying the driver of health problems such as bad health and excessive fatigue.

Loss caused by traffic jams is a serious problem around the globe. For example, the time loss caused by traffic jams in Japan is 3.8 billion hours per year (30 hours per citizen), which is equivalent to ¥11.6 trillion (approx US$96.5 billion). So, there is an urgent need for the prevention of traffic jams.

VICS (vehicle information and communication systems), which are currently used for predicting traffic jams, collect information related to traffic jams by using fixed-point cameras set up on roads. But they are available only on some major arterial roads and expressways. Therefore, there has been a demand for systems that do not depend on infrastructure, predict traffic jams only with data obtained by a vehicle and prevent vehicles from being caught in a traffic jam.

Source: Nikkei/Telematics News

EU to improve trans-european traffic information

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The European Commission has adopted new rules which will help provide road users across the EU with more accurate, accessible and up-to-date traffic information related to their journeys (Real-Time Traffic Information).

This can include information about expected delays, estimated travel times, information about accidents, road works and road closures, warnings about weather conditions and any other relevant information. Such information can be delivered to drivers through multiple channels: variable message signs, radio traffic message channels, smartphones, navigation devices, etc. A functioning market already exists for Real-Time Traffic Information services, which is why the objective of the new rules adopted today, is to make existing information services available to more users, facilitate the sharing of digital data, and foster the availability of more and accurate data.

What are Real-Time Traffic Information Services Real-time traffic information services aim to provide road users with accurate and up-to-date information related to their journeys. This can include information about the road network, traffic regulations (such as speed limits and access restrictions), officially recommended alternative routes, expected delays and estimated travel times, information about accidents, road works and road closures, warnings about weather conditions and any other relevant information (e.g. information about road tolls and availability and cost of parking places at the destination). Real-Time Traffic Information can be delivered to drivers through various channels, such as variable message signs, variable speed limits, radio traffic message channels, smartphones, navigation devices, etc.

Objectives of the Commission action A functioning market already exists for the delivery of Real-Time Traffic Information services to end users. Therefore, the objectives of the Commission action (specifications) are to: – Make existing Real-Time Traffic Information services available to more users by increasing EU-wide interoperability and continuity of data and services (in combination with travel information services); – Increase the existence of (digital) data and its sharing, to decrease data fragmentation and make data more accessible and improve data quality; – Facilitate the availability of more and accurate data to enable better management of road infrastructure and traffic flows. Delegated Regulation (specifications) To improve the interoperability of the data, the specifications require that road status and traffic data are made accessible via national access points in a standardised format.

The specifications also establish rules on data updates including timeliness of these updates. The draft specifications have gone through extensive consultations with the experts nominated by the Member States and other public and private stakeholders. The specifications will apply to the comprehensive Trans-European road network and motorways not included in this network as well as to “priority zones” (especially interurban/urban busy roads) when national authorities voluntarily identify such zones.

The specifications do not make the deployment of Real-Time Traffic Information services obligatory. However, when these services are already deployed in a Member State or will be deployed after the date of application of the delegated regulation, the specifications will have to be followed. The key enabler to the provision of accurate, reliable and content rich Real-Time Traffic Information services is to improve accessibility and interoperability of existing and up-to-date data across the EU. Therefore, the specifications foresee that each Member State sets up a national access point (single window) for the exchange of data. Now that the specifications have been adopted, they will be transmitted to the Council and the European Parliament for their right of scrutiny. The Delegated Regulation will apply from 24 months after its entry into force.

Source: European Commission

Mercedes S 500 Drives 100 KM Autonomously through interurban and urban route

When it sent its S 500 INTELLIGENT DRIVE research vehicle along a historic route in August 2013, Mercedes-Benz became the first motor manufacturer to demonstrate the feasibility of autonomous driving on both interurban and urban routes. The route in question, covering the 100 kilometres or so from Mannheim to Pforzheim, retraced that taken by motoring pioneer Bertha Benz exactly 125 years ago when she boldly set off on the very first long-distance drive. In the heavy traffic of the 21st century the self-driving S-Class had to deal autonomously with a number of highly complex situations – traffic lights, roundabouts, pedestrians, cyclists and trams. It should be noted that this trailblazing success was not achieved using extremely expensive special technology, but with the aid of near-production-standard technology, very similar to that already found in the new E and S-Class. The project thus marks a milestone along the way that leads from the self-propelled (automobile) to the self-driving (autonomous) vehicle.

In August 1888, Bertha Benz set off on her famous first long-distance automobile journey from Mannheim to Pforzheim. In doing so, the wife of Carl Benz demonstrated the suitability of the Benz patent motor car for everyday use and thus paved the way for the worldwide success of the automobile. Precisely 125 years later, in August 2013, Mercedes-Benz recorded a no less spectacular pioneering achievement following the same route. Developed on the basis of the new Mercedes-Benz S-Class, the S 500 INTELLIGENT DRIVE research vehicle autonomously covered the approximately 100 kilometres between Mannheim and Pforzheim. Yet, unlike Bertha Benz all those years ago, it did not have the road “all to itself”, but had to negotiate dense traffic and complex traffic situations.

“This S-Class spells out where we’re headed with “Intelligent Drive” and what tremendous potential there is in currently available technology,” says Dr. Dieter Zetsche, Chief Executive Officer of Daimler AG and Head of Mercedes-Benz Cars. “Of course, it would have been a lot easier to take the autobahn for the autonomous drive from Mannheim to Pforzheim. But there was a special motivation for us to carry out this autonomous drive along this very route 125 years after Bertha Benz. After all, we wouldn’t be Mercedes-Benz unless we set ourselves challenging goals and then went on to achieve them.”

Autonomous driving with production-based sensors

The Mercedes-Benz S 500 INTELLIGENT DRIVE research vehicle was equipped with production-based sensors for the project. Based on a further development of the sensor technologies already in use in the new S-Class, the developers taught the technology platform to know where it is, what it sees and how to react autonomously. With the aid of its highly automated “Route Pilot”, the vehicle is able to negotiate its own way through dense urban and rural traffic.

“For us, autonomous vehicles are an important step on the way to accident-free driving,” says Zetsche. “They will bring greater comfort and safety for all road users. That’s because autonomous vehicles also react when the driver is inattentive or fails to spot something. On top of that, they relieve the driver of tedious or difficult tasks while at the wheel.”

“With our successful test drives following in the tracks of Bertha Benz, we have demonstrated that highly automated driving is possible without the luxury of specially closed-off sections of road and relatively straightforward traffic situations,” says Professor Thomas Weber, member of the Board of Management of Daimler AG with responsibility for Group Research and Head of Mercedes-Benz Cars Development. “In line with the goal of the project, we have gained important insights into the direction in which we need to further develop our current systems in order to enable autonomous driving not just on motorways, but also in other traffic scenarios. Even we ourselves were quite surprised at just how far we got using our present-day sensor technology. But now we also know how much time and effort is needed to teach the vehicle how to react correctly in a host of traffic situations – because every part of the route was different,” adds Weber. This experience will now be incorporated into the engineering of future vehicle generations to be equipped with such innovative, further-developed functions. The Head of Daimler’s Research and Development stresses: “With the new S-Class, we are the first to drive autonomously during traffic jams. We also want to be the first to bring other autonomous functions in series production vehicles. You can expect that we will reach this goal within this decade.”

Several levels of autonomous driving

The main advantages of autonomous driving are plain to see: it allows motorists to reach their destination quickly, safely and in a more relaxed frame of mind. Above all on routine journeys, in traffic jams, on crowded motorways with speed restrictions and at accident blackspots, an autonomous vehicle is capable of assisting the driver and relieving them of tedious routine tasks. However, the intention is not to deprive the driver of the experience and pleasure of doing the driving for themselves. “Our autonomous systems offer to assist and unburden the driver. Those who want to drive themselves are free to do so, and that won’t change in future either,” stresses Daimler development chief Weber. “It’s clear, however, that autonomous driving will not come overnight, but will be realised in stages. With this drive, we’ve now taken another important step into the future.”

A distinction is made between three levels of autonomous driving. These have been defined by a VDA working group in collaboration with the German Federal Highway Research Institute (BASt): partially, highly and fully automated.

  • With partially automated driving, the driver must constantly monitor the automatic functions and must not pursue any non-driving-related activity.
  • In the case of highly automated driving, the driver need not permanently monitor the system. In this case, non-driving-related activities are conceivable on a limited scale. The system recognises its limitations by itself and passes the driving function back to the driver with sufficient time to spare.
  • With fully automated driving, the system is capable of autonomously coping with every situation; the driver need not monitor the system and can pursue non-driving-related activities. Equally, driverless driving is possible at this level.

Partially automated driving is already available to drivers of new Mercedes-Benz E and S-Class models: the new DISTRONIC PLUS with Steering Assist and Stop&Go Pilot is capable of steering the vehicle mainly autonomously through traffic jams. This system thus forms the core of “Mercedes-Benz Intelligent Drive”, the intelligent networking of all safety and comfort systems on the way to accident-free and, ultimately, autonomous driving.

The now successfully conducted autonomous test drives along the Bertha Benz route allowed the Daimler researchers to gather important information on the challenges that remain to be addressed on the way to highly and fully automated driving and what, for example, still needs to be done to enable a car to navigate safely in highly complex situations involving traffic lights, roundabouts, pedestrians and trams.

Initial road tests using technology platforms based on the E and S-Class

Unnoticed by the public, yet authorised by appropriate official exemptions and certificates from the TÜV (German Technical Inspection Authority), testing of the “Route Pilot” on the Bertha Benz route began in early 2012 with a total of three technology platforms based on the E and S-Class, which are equipped with all available active and passive safety systems.

These test vehicles employed only those sensor technologies that are already today used in similar form in Mercedes-Benz standard-production vehicles. This is because those technologies are already affordable and suitable for everyday use, which facilitates a possible transfer to subsequent standard-production models. However, improvements were made to the number and arrangement of the sensors in order to achieve comprehensive coverage of the vehicle’s surroundings in every direction, and to obtain additional information on the area around the vehicle.

Based on these sensor data and determination of the vehicle’s own position with reference to information from a digital map, an autonomously driving vehicle analyses the available free area for driving and plans its own route. The required algorithms were developed by the Mercedes-Benz research team in collaboration with the Institute for Measuring and Control Technology at the Karlsruhe Institute of Technology (KIT).

The specific technical modifications compared with the standard-production version of a Mercedes-Benz S-Class are as follows:

  • The base width (distance between the eyes) of the stereo camera was increased to allow more-distant objects to be detected not only by the radar, but also by the camera.
  • Two additional long-range radars were installed at the sides of the front bumpers to provide early detection of vehicles coming from the left or right at junctions. A further long-range radar monitors the traffic to the rear.
  • Four short-range radars at the corners of the vehicle provide improved detection of the nearer surroundings and other road users.
  • Traffic lights are monitored by a colour camera behind the windscreen with a 90-degree opening angle.
  • Another camera looks towards the back through the rear window to locate the vehicle with reference to known environment features. These environment features were previously entered on a digital map. By comparing what has just been seen by the camera with what is stored on the map, the vehicle is able to locate its position with significantly greater accuracy than would be possible with GPS alone.

For the trip along the Bertha Benz route, Mercedes-Benz collaborated with KIT and HERE, a division of Nokia specialised in the production of digital maps and location-specific services, to produce a 3D digital map of the route between Mannheim and Pforzheim that was specifically adapted to the requirements of an autonomous vehicle. In addition to the road layout, this map – which must meet special requirements with regard to accuracy – includes information on the number and direction of traffic lanes and traffic signs as well as the positions of traffic lights. Digital maps of this kind are a key prerequisite for autonomous driving. Mercedes-Benz and HERE will therefore continue their collaboration in future with regard to the development of “intelligent” 3D digital maps for autonomous vehicles.

Route Pilot reacts to diverse traffic situations

The Route Pilot in the research vehicle is required to cope with a host of different challenges both on country roads and in urban traffic: roundabouts, obstructions in built-up areas with oncoming traffic, cyclists on the road, turn-off manoeuvres, variously parked vehicles, red traffic lights, “right before left” priority junctions, crossing pedestrians and trams.

The autonomously driving S-Class was monitored during the tests by specially trained safety drivers who, whenever the system made an incorrect decision, were able to intervene immediately and take over control of the vehicle. As real traffic is unpredictable – which means that no driving situation is exactly the same as an earlier one – a record was made each time it became necessary for the safety driver to take over control of the vehicle. This information was then evaluated by the development team, thus making it possible to extend the vehicle’s repertoire of manoeuvres. This advances the development of the technology platform, enabling it to cope with more and more traffic situations.

The test drives along the 100-kilometre-long route deliver important information for further development of the technology and the product. “For example, it became apparent that the recognition of traffic light phases under different lighting conditions and the correct pairing of individual traffic lights with traffic lanes represents a major challenge,” explains Prof. Ralf Herrtwich, head of driver assistance and suspension systems at Daimler Group Research and Advance Development, a role in which he initiated the autonomous driving project. “However, it is not our intention that the vehicle should master every situation on its own. If, for example, the road is blocked by a refuse collection vehicle, we certainly don’t want the vehicle to automatically overtake it, especially as the vision of the vehicle’s sensors is restricted in such a case. In such a situation, the vehicle passes control back to the driver.”

For the company, the success of the autonomous road tests lies above all in having identified those areas on which the development team needs to concentrate in future. “We now know where we can make further improvements and refinements to the vehicle’s repertoire of programmed manoeuvres, i.e. the situation-dependent control commands for steering, engine and brakes, such as how to autonomously negotiate a roundabout.” A further challenge is to correctly locate the vehicle on the road, in order to determine, for example, precisely where a vehicle should stop at a junction while at the same time having a view of cross-traffic.

A particular challenge for autonomous vehicles is the way in which they communicate and interact with other road users. Coming to an agreement with an oncoming vehicle on who should proceed first around an obstruction is something that requires a very great deal of situational analysis. “Where a human driver might boldly move forward into a gap, our autonomous vehicle tends to adopt a more cautious approach,” says Herrtwich. “This sometimes results in comical situations, such as when, having stopped at a zebra crossing, the vehicle gets waved through by the pedestrian – yet our car stoically continues to wait, because we failed to anticipate such politeness when we programmed the system.”

To enable the developers to reconstruct the decisions made by the autonomous research vehicle in individual driving situations, the car makes recordings of all its sensor data. Images from the stereo camera alone generate 300 gigabytes of data every hour. Also in later standard operation, some of these data will continue to be stored. That’s because if, for example, an autonomous vehicle is involved in an accident, this information will make it possible to establish what happened.

Challenges on the path to autonomous driving

Before the goal of highly and fully autonomous driving is achieved, the obstacles to be overcome will not be just of a technical nature. Many of the things that are already technically feasible are still not universally permitted.

For instance, international UN/ECE Regulation R 79 (steering systems) allows only corrective steering functions, but not automatic steering at speeds above 10 km/h. Under the Vienna Road Traffic Convention, which is relevant for EU law, the driver must be in constant control of their vehicle and be capable of intervening at all times. As autonomous vehicles were still out of the question at the time this convention was adopted, clarification is needed with regard to what this means for highly or fully automated vehicles. In some US states such as Nevada, there has already been such clarification, at least as far as the trial operation of autonomous vehicles is concerned. Another prerequisite for the transition from partially to highly automated systems is their acceptance in society. Just as when the automobile was originally invented, it will first of all be necessary to build up confidence in the technical capabilities of the systems. This is borne out by a recent study carried out by the Customer Research Centre at Mercedes-Benz involving around 100 test persons aged between 18 and 60. The initial scepticism of the study participants was almost entirely dispelled following an autonomous drive in the driving simulator. Even among those participants who were negatively disposed to begin with, there was a significant increase in acceptance after the drive in the simulator.

One way of ensuring that map data and route information is always kept up to date is to use “Car-to-X Communication”. This could enable future vehicles to help each other to generate real-time maps, because, theoretically, every car is capable of recording the route it has driven and entering it in a database. Information on a red traffic light could be relayed from a waiting car to other road users. Alternatively, the traffic light itself could send a signal to nearby vehicles. Mercedes-Benz has been working for several years on communication between vehicles and between vehicles and their environment. This year, it is set to become the first manufacturer to bring “Car-to-X functions” onto the market.

PROMETHEUS – pioneering achievement on the way to autonomous driving

Mercedes-Benz’s success on the Bertha Benz route is the latest result of years of research in the field of autonomous driving. An earlier milestone was the Daimler-Benz-initiated research project EUREKA-PROMETHEUS (“Programme for European Traffic with Highest Efficiency and Unprecedented Safety”), which ran from 1986 and whose test vehicles made headlines when, in 1994, in normal traffic, they covered around 1000 kilometres, mainly autonomously, on a multi-lane motorway in the Paris region and then, in 1995, drove from Munich to Copenhagen. Consequently, almost 20 years ago, Mercedes-Benz demonstrated that automated driving on motorways, including lane-changing, overtaking and keeping a safe distance, is technically feasible.

One of the outcomes of the Prometheus project was DISTRONIC adaptive cruise control, which went into production in the S-Class in 1998. Based on DISTRONIC, Mercedes-Benz has developed a succession of assistance systems capable of detecting hazardous situations, warning the driver and, ever more frequently, also automatically intervening. The project also resulted in Speed Limit Assist, which went into series production in 2005. Continuous further advances in environment detection using stereo cameras, also first tested as part of PROMETHEUS, created the foundation for the “6D Vision” stereo camera, which has now been launched in the new E- and S-Class. Patented by Daimler, this technology makes it possible to anticipate the real-time movements of other nearby road users.

At a technical level, Prometheus and the Mercedes-Benz S 500 INTELLIGENT DRIVE are worlds apart. “Progress has been due above all to modern-day hardware and software, which have been the focus of targeted optimisation over the years,” explains Mercedes-Benz development chief Weber. “Technical components in those days were much too big and much too expensive for standard use in automobiles. Also, they were not powerful or reliable enough. The situation today is a quite different one. Our modern systems can be installed in compact control units that, while exceptionally powerful, are still affordable. Because that’s the only way in which the maximum possible number of customers can benefit from autonomous vehicle functions – and that’s our ultimate goal.”

Mercedes-Benz assistance systems with partially automated driving functions in standard-production vehicles

  • DISTRONIC/DISTRONIC PLUS adaptive cruise control (1998/2005) Launched in 1998 and further developed in 2005 with improved radar sensors, adaptive cruise control automatically maintains a safe distance from the vehicle in front. It is capable of autonomous braking and acceleration.
  • PRE-SAFE Brake (2006) Automatically brakes the vehicle if there is a risk of a rear-end collision (autonomous partial/emergency braking).
  • Active Blind Spot Assist (2010) Detects whether there is a vehicle in the driver’s blind spot and, by means of one-sided application of the brakes, can reduce the risk of collision from a change of lane.
  • Active Lane Keeping Assist (2010) Networked with ESP. If the driver unintentionally crosses a continuous or discontinuous lane marking, Active Lane Keeping Assist can brake the wheels on the opposite side, thereby returning the vehicle to the original lane.
  • Active Parking Assist (2010) Uses electromechanical direct steering to navigate the vehicle into a parking space.
  • DISTRONIC Plus with Steering Assist and Stop&Go Pilot (2013) Helps the driver not only to maintain a desired distance from the vehicle in front, but also to stay in the centre of the lane. This makes it possible to autonomously follow vehicles in traffic tailbacks.
  • BAS PLUS Brake Assist with Cross-Traffic Assist (2013) Capable of detecting cross-traffic and pedestrians and boosting the braking power applied by the driver.

Source: Mercedes