{"id":8821,"date":"2019-11-20T11:26:12","date_gmt":"2019-11-20T16:26:12","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=8821"},"modified":"2026-03-26T06:02:16","modified_gmt":"2026-03-26T10:02:16","slug":"virtual-development-and-demonstration-of-advanced-driver-assistance-systems","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/virtual-development-and-demonstration-of-advanced-driver-assistance-systems\/","title":{"rendered":"Virtual Development and Demonstration of Advanced Driver Assistance Systems"},"content":{"rendered":"\n

How long until truly autonomous vehicles are on the road? Technology is advancing in vehicles at a very fast rate and new features are constantly being added in an effort to enhance comfort, convenience and safety. Numerous manufacturers have their version of auto-pilot, lane departure assist, adaptive cruise control, automated parallel parking among others. These enhancements are known as Advanced Driver Assistance Systems (ADAS). These systems aid the driver while driving the vehicle, and control some of the behavior of the car directly. This helps minimizing human error and thus reduces injuries and fatalities. <\/p>\n\n\n\n

One of the first challenges is that the design, development,\ntesting and validation of ADAS functionality is complex and requires a lot of\ntime, money and effort to complete. This is where virtual verification and\nvalidation come into play; to be able to virtually evaluate the vehicle\nbehavior and performance across a range of scenarios at a drastically reduced timeframe\nand cost. <\/p>\n\n\n\n

The second challenge is that new vehicle designs equipped\nwith advanced driver assistance systems need to be validated with respect to\nhuman perceptions of comfort and risk. Therefore, human-in-the-loop simulations\nare used to evaluate a wide range of scenarios in driving simulators. <\/p>\n\n\n\n

ENABLE-S3 project: virtual\ntesting & verification – accelerated application of highly automated and\nautonomous systems<\/strong><\/p>\n\n\n\n

The European Commission is funding R&D to support progress in this technology field, amongst others with the EC ECSEL\/H2020 research project \u201cENABLE-S3<\/a>\u201d. This project was a collaboration of a large consortium with more than 70 partners that focused on improving verification and validation methods of autonomous driving functions for ground, air and marine vehicles. <\/p>\n\n\n\n

In the ENABLE-S3 project, Siemens Digital Industries Software has worked out solutions to address both aforementioned challenges. Firstly, by delivering a simulation platform as a virtual verification and validation environment that is capable of modeling software, hardware and driver\/human-in-the-loop. Secondly, by enabling the driving simulator (See the previous blogpost<\/a>) to offer a highly realistic driving experience to the human driver by using Simcenter Prescan, Simcenter Amesim and Simcenter 3D to model the environment, the vehicle and its components. <\/p>\n\n\n\n

To show our capability, let\u2019s dive into three Use Cases that\nhave been evaluated in the ENABLE-S3 project: 1) Adaptive Cruise Control (ACC)\non highway, 2) Intersection Crossing and 3) Valet Parking. They are executed on\nthe virtual demonstration environment in Simcenter Prescan shown in Figure 1.\nWithin this virtual environment, the vehicle and its components are modelled\nwith Simcenter 3D and Simcenter Amesim. This offers realistic vehicle dynamics behavior\nin Simcenter 3D with an accurate representation of active and adaptive\ncomponents in Simcenter Amesim. <\/p>\n\n\n\n

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Figure 1: Virtual demonstration environment in Simcenter Prescan for the 3 Use Cases: 1) Adaptive Cruise Control (ACC) on highway, 2) Intersection Crossing, 3) Valet Parking. <\/figcaption><\/figure>\n\n\n\n

Adaptive Cruise\nControl (ACC) on highway <\/strong><\/p>\n\n\n\n

The first automated driving functionality (ADF) investigated\nand used on the simulator for demonstration is Adaptive Cruise Control (ACC),\nwhich is a Level 1 ADF according the SAE definition on vehicle autonomy. This\nsystem keeps the vehicle at a constant velocity set by the driver (cruise\ncontrol mode). When a slower leading car is approached, the system automatically\nadapts the velocity and keep a predefined distance to avoid a rear-end\ncollision. The actions taken by the ACC control algorithm are based upon the\ninformation given by two scanning sensors: the long range radar (LRR) and the\nshort range radar (SRR). These sensors continuously evaluate the presence of a\nlead car. In the case a slower car gets inside the sensor measuring range, the\ncontrol procedure computes the relative distance and speed between the two\nvehicles. The brake system is then triggered appropriately to slow down, in\norder to maintain a safe constant distance. <\/p>\n\n\n\n

In the demo case, a straight highway piloting scenario is\nconsidered. In this scenario, the ego vehicle (which means, the vehicle with\nthe autonomous driving algorithms under consideration) is cruising at\napproximately 100 km\/h and approaching a lead vehicle. To avoid the collision,\nthe ego vehicle slows down to the same speed as the lead vehicle as is shown in\nFigure 2. After 41 seconds, the ego vehicle decides to overtake the lead\nvehicle. From this moment, the ACC increases the velocity of the ego vehicle to\nthe previously defined setpoint. <\/p>\n\n\n\n

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Figure 2. Results ACC demo case. <\/figcaption><\/figure>\n\n\n\n

Intersection Crossing<\/strong><\/p>\n\n\n\n

The second ADF implemented on the simulator for demonstration\nis an autonomous intersection crossing, which is a Level-2 ADF. In this case, a\ncross-road scenario is chosen and it is assumed that two vehicles are\napproaching the intersection, both with a predefined trajectory. The autonomous\nintersection crossing functionality comprises of a lateral controller for being\nable to make turns and longitudinal controller for velocity adjustment. For the\ncollision avoidance at the intersection, an algorithm is adopted and\nimplemented that assumes that both vehicles are following a predefined\ntrajectory, which makes the problem solvable by only changing longitudinal\nvelocity. Therefore, the algorithm forces the ego vehicle to either brake or\naccelerate in order to avoid a collision. What will happen is that one vehicle\nwill try to exit the intersection as fast as possible and the other will try to\nslow down as much as possible to avoid a collision between both vehicles. <\/p>\n\n\n\n

The autonomous intersection crossing controller is\ndemonstrated in the second scenario. The developed control algorithm is\nvalidated by having the ego vehicle approaching a cross-road together with one\nother road user. At a certain moment, their trajectories are overlapping and a\ncollision might occur. Both vehicles are making a left turn and there is no\ncommunication between the road users. In this scenario, the ego vehicle arrives\nfirst at the intersection. It decides to slow down and give the priority to the\nother vehicle, because there is a chance of collision. After the road user\nleaves the intersection, the ego vehicle accelerates and makes a left turn. The\nvelocities and control inputs are shown in Figure 3. <\/p>\n\n\n\n

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Figure 3. Velocity and control inputs of the two vehicles approaching the intersection. <\/figcaption><\/figure>\n\n\n\n

Valet Parking <\/strong><\/p>\n\n\n\n

In this case, the path planning and path following\nalgorithms for reverse parking are implemented which is a Level-4 ADF according\nto SAE definition. This work is based on previous research on valet parking.\nThe path planning uses a so-called \u201cHybrid A*\u201d algorithm to find the shorted\nfeasible path from the current position to the selected destination. This\nplanning is first performed before the start of the maneuver. In case the path\ncannot be followed, e.g. in case of an unexpected obstacle, the vehicle is\nautomatically stopped and the path is recomputed. Once the path is available,\nthen a path following controller is used in real-time to move the vehicle. <\/p>\n\n\n\n

The final scenario in the demonstrator is a reverse parking maneuver\nthat requires a reverse turn of 90 degrees. The vehicle is driving to a\nposition near an empty parking spot. The middle of the parking spot is used as\nthe goal of the path planning algorithm. The obtained path is shown in blue in\nfigure 4. Once the path is available, the path following controller is started.\nThis selects the reverse gear and drives the vehicle to the selected parking\nspot. Because the planned path is computed with a more simplified model, this\npath is not followed perfectly, as the actual vehicle has a more complex\nbehavior compared to the simplified model. This is replicated in the virtual\nenvironment with the use of the high-fidelity vehicle model in Simcenter 3D that\nuses the steer wheel as input. <\/p>\n\n\n\n

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Figure 4. Vehicle trajectory and planned path of reverse parking maneuver. <\/figcaption><\/figure>\n\n\n\n

Conclusions<\/strong><\/p>\n\n\n\n

A virtual demonstration environment has been achieved, which\nallows the virtual validation of an ADAS for different variants of vehicle,\nenvironment models, sensor models, decision making algorithms or driver\nbehaviors. The inherent flexibility and scalability of the platform allows\nengineers to test many scenarios, available models (vehicle, sensor or\ncontroller), and weather and traffic conditions. This virtual design,\ndevelopment and testing approach helps to increase trust and confidence in the\ndeveloped algorithms and technologies, and ultimately to increase the safety\nboth for the test operators, road actors and infrastructure. With this\ntechnology, it becomes feasible to virtually test scenarios, which are\ndifficult and often impossible or too expensive to test in real life, such as\ncertain weather conditions, and fault\/failure of components. Three Use Cases\nhave been presented that show that realistic vehicle active safety and\nautonomous driving scenarios can be covered virtually. <\/p>\n\n\n\n

The platform will be used in the future to perform a variety of research activities, including development of human machine interfaces for future autonomous vehicles, human perception in autonomous vehicles, driver training for autonomous vehicles, model-based testing and validation.<\/p>\n\n\n\n

Acknowledgements<\/strong><\/p>\n\n\n\n

Siemens would like to acknowledge the ENABLE-S3 project that\nhas received funding from the ECSEL Joint Undertaking under grant agreement No.\n692455. This joint undertaking receives support from the European Union\u2019s\nHORIZON 2020 research and innovation programme and Spain, Portugal, Poland,\nIreland, Belgium, France, Netherlands, United Kingdom, Slovakia, Norway.<\/p>\n","protected":false},"excerpt":{"rendered":"

How long until truly autonomous vehicles are on the road? Technology is advancing in vehicles at a very fast rate…<\/p>\n","protected":false},"author":6552,"featured_media":8829,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[5,21],"industry":[89],"product":[577,590,511],"coauthors":[],"class_list":["post-8821","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-cae-simulation","tag-technology-innovation","industry-automotive-transportation","product-simcenter-3d","product-simcenter-amesim","product-simcenter-prescan"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/6\/2019\/11\/GettyImages-960714156.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/8821","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/users\/6552"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/comments?post=8821"}],"version-history":[{"count":4,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/8821\/revisions"}],"predecessor-version":[{"id":8832,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/posts\/8821\/revisions\/8832"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media\/8829"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/media?parent=8821"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/categories?post=8821"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/tags?post=8821"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/industry?post=8821"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/product?post=8821"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/simcenter\/wp-json\/wp\/v2\/coauthors?post=8821"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}