{"id":3079,"date":"2020-02-11T14:56:31","date_gmt":"2020-02-11T19:56:31","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/?p=3079"},"modified":"2026-03-26T12:03:10","modified_gmt":"2026-03-26T16:03:10","slug":"how-automakers-can-keep-autonomous-vehicles-safe-in-bad-weather","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/how-automakers-can-keep-autonomous-vehicles-safe-in-bad-weather\/","title":{"rendered":"How automakers can keep autonomous vehicles safe in bad weather"},"content":{"rendered":"\n

Today meets tomorrow\n\u2013 simulating the impact of harsh conditions on ADAS sensors<\/strong><\/p>\n\n\n\n

Recently, an OEM was working to modify their side view\nmirror and their team was tasked with making changes, any changes they deemed\nnecessary, while preserving the design and positioning of the camera and mount\non the mirror. The challenge was to prevent film from dirt or water from\nbuilding up on the camera. <\/p>\n\n\n\n

According to a McKinsey&Company<\/a> report, by 2030, 15 percent of the new cars entering the auto market may be autonomous and 15-50 percent will likely be electrified vehicles (Figure 1). While this is just a decade away, there are still monumental challenges that automakers face. One in particular involves the advanced driver assistance system (ADAS) sensors and their ability to function in adverse weather conditions or if dirt soils the sensors.<\/p>\n\n\n\n

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Fig. 1: A glance at the auto market in 2030<\/figcaption><\/figure><\/div>\n\n\n\n

For anyone driving in harsh weather conditions, this is an\nall-too-familiar sight \u2013 the rain and snow or the road\u2019s dirt and wintertime\nsalt soiling the windshield. The safety of the vehicle depends on your ability\nto see. The sensors in an autonomous vehicle must be able to \u201csee\u201d at all times\nas well.<\/p>\n\n\n\n

The last thing you want to do is be driving and have your sensors suddenly indicate they\u2019re malfunctioning because of too much snow, rain or mud.<\/p>\n\n\n\n

In the webinar, Massive Verification and Validation of ADAS\nand Autonomous Driving Vehicles<\/em><\/a>, one of the intriguing points brought\nup regarded autonomous capabilities on not just dry days or when it\u2019s light,\nbut harsh weather conditions as well.<\/p>\n\n\n\n

There was a simulation example of a backup camera in which water was impacting what could be seen by both a human or an ADAS sensor (Figure 2). In the example shown (figure to be provided), it was difficult to see a human stepping into the parking spot behind the vehicle as it was backing in. This error put the safety of the individual at risk. These situations may sound familiar to anyone who has tried backing out of a parking spot on a rainy night but is unacceptable if we\u2019re to fully adopt autonomous vehicles.<\/p>\n\n\n\n

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Fig 2: Back-up camera in adverse weather conditions<\/figcaption><\/figure><\/div>\n\n\n\n

Enhancing the visibility for both the human eye and the\nsoftware is a major challenge that must be addressed. To have the capability of\nentering the requirements into a simulation and a comprehensive digital twin\nallows engineers to investigate and see where the design needs improvements and\nensure whoever or whatever is making use of the camera and the sensors has the\nability to make decisions with near-zero risk of harming anyone or anything.<\/p>\n\n\n\n

By discovering ways to limit the water or dirt on the camera\nor sensor, engineers and design teams can better improve the performance of the\nautonomous system itself.  What you can\ndo to see if you\u2019ve met the requirements is to take the results, put it into\nthe software and make sure you\u2019re meeting the requirements of the design.<\/p>\n\n\n\n

Managing water and\ndirt<\/strong><\/h2>\n\n\n\n

The first priority in aerodynamic design is the styling and\nperformance of the vehicle itself for which you want to try to minimize energy\nuse. This entails drag reduction, increasing the drive range for an electric\nvehicle and improved energy management, which includes enhancing cooling\nperformance.<\/p>\n\n\n\n

The second priority is managing the acoustics of the vehicle,\ncan involve noise from the side mirror, open side window throb, sunroof\nbuffeting, underbody noise or HVAC noise.<\/p>\n\n\n\n

The final impact on aerodynamic performance, and the one\nwe\u2019re going to take a closer look at, is water management. This includes water\nimpact wiper and sensor performance as well as its affects on sideview mirrors\nand cameras.<\/p>\n\n\n\n

Water and dirt management has so many factors that it\u2019s a\ncomplex problem to solve, but not impossible. Multiphysics simulation is being\nused to determine everything from a vehicle\u2019s reaction from hitting a puddle to\nwipers swiping away water. With increasing autonomous features, managing how\nsensors react to adverse conditions is a critical step on the path to full\nautonomy. What multiphysics simulation must do is capture the correct physics\nby accurately modeling air and water interactions, ensure a quick turn-around so\nthere\u2019s an impact design before creating the first prototype and enable design\nexploration.<\/p>\n\n\n\n

One of the most complex sensor problems to solve involves\nthe one behind the windshield, which is more apt to face head-on rain, snow,\ndirt and shifting windshield wipers. For this level of simulation and digital\ntwin, you have to recognize that the sensor behind a windshield is not only\nreacting to the adverse conditions like multiple layers of precipitation\nfilming up but the layers of glass in its way.  The first simulation conducted should set a\nbaseline of its performance in optimal conditions and then make design changes\nto see how it impacts the design itself.<\/p>\n\n\n\n

From there, the next challenge is creating a realistic\nsimulation of interaction between all aspects, such as disperse phase of the\nwater, external flow field, fluid film modelled as 2D on the surface, three-dimensional,\ncomplex motion and randomly driven influences like wind.<\/p>\n\n\n\n

With a comprehensive suite of multiphase models, you can\naccomplish simulation and build an accurate digital twin. <\/p>\n\n\n\n

Simulating the\nwindshield wiper<\/strong><\/h2>\n\n\n\n

Water on the windshield has complex physics that must be\nconsidered within the simulation. For instance, water appears not only as\nparticles but also as rivulets, continuous films and pools, and moves from\nthere. This means the sensors behind the windshield aren\u2019t necessarily going to\nbe where the primary impingement of droplets take place. Instead the secondary\nfilm rivulets, how they roll past the sensor as the wind or wipers push them\noff the windshield, are critical factors that impact both safety as well as risk\nof failure.<\/p>\n\n\n\n

The complex motion of the wiper blade moving back and forth\nfor the simulation seems straightforward, but in reality, you have to make sure\nthe wiper blade is sliding on the curved windshield surface. This is\naccomplished by using an overset mesh approach. In this case, it would be a stationary,\nfixed background mesh around the car and the moving overset regions \u2014 the\nwipers.<\/p>\n\n\n\n

Then the digital twin and simulation would use mesh morphing\nbroken down as:<\/p>\n\n\n\n