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Trajectory Planning and Control for an Autonomous Race Vehicle

Autonomous vehicle technologies offer potential to eliminate the number of traffic accidents that occur every year, not only saving numerous lives but mitigating the costly economic and social impact of automobile related accidents. The premise behind this dissertation is that autonomous cars of the near future can only achieve this ambitious goal by obtaining the capability to successfully maneuver in friction-limited situations. With automobile racing as an inspiration, this dissertation presents and experimentally validates three vital components for driving at the limits of tire friction. The first contribution is a feedback-feedforward steering algorithm that enables an autonomous vehicle to accurately follow a specified trajectory at the friction limits while preserving robust stability margins. The second contribution is a trajectory generation algorithm that leverages the computational speed of convex optimization to rapidly generate both a longitudinal speed profile and lateral curvature profile for the autonomous vehicle to follow. The final contribution is a set of iterative learning control and search algorithms that enable autonomous vehicles to drive more effectively by learning from previous driving maneuvers. These contributions enable an autonomous Audi TTS test vehicle to drive around a race circuit at a level of performance comparable to a professional human driver. The dissertation concludes with a discussion of how the algorithms presented can be translated into automotive safety systems in the near future.

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Study: how will self-driving vehicles change Moscow?

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Posted: 7 May 2020 | Sam Mehmet (Intelligent Transport)

Based on current analysis, researchers have created future scenarios for Moscow to determine how self-driving vehicles will impact ride sharing, public transport, passenger experience and everyday life.

Moscow's MaaS platform will unite various car sharing, scooter rental and taxi operators.

Higher School of Economics University (HSE) researchers have assessed the effects of self-driving cars on the city of Moscow. In their study, Alexei Zomarev and Maria Rozhenko presented predictions for 2030 and 2035 based on an appropriate analysis for the city of Moscow.  

In the coming decade, they predicted that self-driving vehicles will come into wider use, stating that researchers are looking more and more at not just driverless cars themselves, but at the potential for them to become shared autonomous vehicles (SAV).

This potential affects two contemporary services, the HSE researchers explained: ride sharing (in which travellers share a vehicle for travel along a similar route) and car sharing (short-term car rental).

Zomarev and Rozhenko created models of how Moscow will look in the near future. Based on official city strategies and available data regarding the number of passengers per car, auto sales, the efficiency of road networks, and so on, the researchers created four scenarios for 2030 and 2035:

• Robotisation
• Absolute Mobility.

The scenarios are said to be characterised by different rates of integrating driverless technology into city transport, as well as different possible states of the public vehicle market, including both traditional and self-driving vehicles.

The models are based on 2022 marking the beginning of the implementation of driverless taxi services, and 2024 as when self-driving cars will be permitted for private use.

Stagnation and Robotisation

The pace at which self-driving vehicles will take over Moscow is different in these scenarios, they explained, but the speed at which shared car services become used more widely is equally slow.

Due to weaker technological development, the rate of automobile accidents in the Stagnation Scenario will decrease to a lesser extent than in other scenarios, with most cars in the city not being self-driving. In the Robotisation Scenario, insufficient quality control over autonomous transport will pose a serious obstacle, they said.

By 2035, according to both scenarios, the number of cars will reach up to six million (compared to 4.7 million in 2019). While there are currently 293 cars per every 1,000 people in Moscow, this number will reach up 464 by 2035. They noted.

Road congestion will increase by 13 per cent, and time spent by drivers in traffic jams will increase by 5-10 per cent. The shortage of parking spaces will increase by 1.7 million.

Absolute Mobility and Shared Use

Unlike the previous scenarios, these scenarios are said to be characterised by a large proportion of shared vehicles. In the Shared Use Scenario, the rate of integration of driverless technology into car sharing is low; sharing services instead rely mainly on human drivers. In the Absolute Mobility Scenario, the rate is high: most transport will be carried out by self-driving vehicles, and the effects of car sharing will reach their optimal potential by 2035:

• The number of passengers per car will double up to 2.3 passengers
• The average time spent per trip in Moscow will be 55 minutes, which is comparable to the average time spent per trip using a personal vehicle
• Daily car usage will increase from today’s average of six trips per day to almost 14 trips per day
• Up to 32 people will use one SAV per day.

All this will reduce city residents’ need for personal vehicles, save time, and eliminate the need to worry about parking (if parking privileges for shared cars are preserved), according to the researchers.

However, they said that self-driving cars will lead to job losses for 200,000 people, including drivers, couriers, traffic controllers, and traffic police, and that traditional public transport will have fewer passengers, and some routes will be eliminated.

“Scenarios with a high proportion of shared vehicles will enable a smaller fleet of vehicles to satisfy a greater demand for passenger transportation. Measures should be introduced gradually and announced in advance, several years before the decisions go into effect,” they concluded.

Related topics Air Quality , Connected & Autonomous Vehicles , Infrastructure & Urban Planning , Passenger Accessibility , Passenger Experience , Public Transport , Transport Governance & Policy , Vehicle & Passenger Safety

Related modes Autonomous vehicles

Related cities Moscow

Related organisations Higher School of Economics University (HSE)

Related people Alexei Zomarev , Maria Rozhenko

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Autonomous vehicles have been highly anticipated because of the possibility that they will greatly reduce or perhaps eliminate the collisions that cause more than one million deaths each year . But safety isn’t the only potential benefit self-driving cars can offer: Teams of researchers around the world are showing that autonomous vehicles can also drive more efficiently than humans can. A U.S. Department of Energy program called NEXTCAR (Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles), for example, is betting that a mix of new smart vehicle technologies can boost fuel efficiency by as much as 30 percent.

As part of the NEXTCAR program, San Antonio, Texas-based Southwest Research Institute (SwRI) showcased advances in autonomous vehicle technology that will improve vehicles’ fuel economy—including the fuel efficiency of non -autonomous automobiles that just so happen to be in traffic with autonomous ones. The demonstration was held at the ARPA-E Energy Inovation Summit in Dallas in late May.

Making an Efficient Autonomous Vehicle

The SwRI team retrofitted a 2021 Honda Clarity hybrid with basic autonomous features such as perception and localization. On the day of the summit, they drove the vehicle along a route encircling the parking lot of the convention center where the summit was held. SWRI’s Ranger localization system , which the researchers installed on the Honda, has a downward-facing camera that captures images of the ground. By initially mapping the driving surface, Ranger can later localize the vehicle with centimeter-level accuracy, using the ground’s unique “fingerprint” combined with GPS data. This precision ensures the vehicle drives with exceptional control.

“It’s almost like riding on rails,” says Stas Gankov , a researcher in SwRI’s powertrain engineering group. For this project, his group collaborated with other divisions at the institute, such as the intelligence systems division, which developed the autonomy software stack added to the Honda Clarity.

Just as important, however, was the addition of an eco-driving module, a key innovation by SwRI. The eco-mode determines the most economical driving speed by considering various factors such as traffic lights and surrounding vehicles. This system employs predictive control algorithms to help solve a tricky optimization problem: How can cars minimize energy consumption while maintaining efficient traffic flow? SwRI’s eco-mode aims to reduce unnecessary acceleration and deceleration in order to optimize energy usage without impeding other vehicles.

“Autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them.” —Stas Gankov, Southwest Research Institute

To illustrate how the technology works, the team installed a traffic signal along the demonstration pathway. Gankov says an actual traffic light timer from a traffic signal cabinet was connected to a TV screen, providing a visual for attendees. A dedicated short range communications (DRSC) radio was also attached, broadcasting the signal’s phase and timing information to the vehicle. This setup enabled the vehicle to anticipate the traffic light’s actions far more accurately than a human driver could. For instance, Gankov says, if the Honda Clarity was approaching a red light that was about to turn green, it would know the light was due to change and so avoid wasting energy by braking and then accelerating again. Conversely, if the car was approaching the signal as it was about to turn from green to yellow to red, the vehicle would release the accelerator and let friction slow it to a crawl, avoiding unnecessary acceleration in an attempt to beat the light.

These autonomous driving strategies can lead to significant energy savings, benefiting not just the autonomous vehicles themselves, but also the entire traffic ecosystem.

“In a regular traffic situation, autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them,” says Gankov. “The result is that even vehicles with Level 0 autonomy use fuel more sparingly.”

The Grand Vehicle Energy Plan

SwRI has been a participant in the NEXTCAR initiative since 2017 . The program’s initial phase involved 11 teams, including SwRI, Michigan Technological University , Ohio State University , and the University of California Berkeley . SwRI, in collaboration with the University of Michigan , focused on optimizing a Toyota Prius Prime, already known for its fuel efficiency, to achieve a 20 percent improvement in energy usage through optimization algorithms and wireless communicating with its surroundings . This was accomplished without modifying the Toyota’s powertrain or compromising its emissions. The team utilized power split optimization, balancing the use of the gas engine and battery propulsion system for maximum efficiency.

Building on the success of NEXTCAR’s first phase, the program entered its second phase in 2021 , with just SwRI, UC Berkeley, Michigan Tech and Ohio State remaining. The focus of NEXTCAR 2 has been determining how much automation could further enhance energy efficiency. Gankov explains that while the first phase demonstrated a 20 percent energy efficiency improvement over a baseline 2016 or 2017 model year vehicle with no autonomous driving capabilities, through the addition of vehicle-to-everything connectivity alone, the second phase is exploring the potential for an additional 10 percent improvement by incorporating autonomous features.

Gankov says SwRI initially intended to partner with Honda for NEXTCAR’s second phase, but when contracting issues arose, the nonprofit proceeded independently. Utilizing an autonomy platform developed by SwRI’s intelligence systems division, the NEXTCAR team equipped the Honda Clarity with what amounted to Level 4 autonomy in a box. This autonomy system features a drive-by-wire system, allowing the vehicle to automatically adjust its speed and steering based on inputs from the autonomy software stack and the eco-driving module. This ensures the vehicle prioritizes safety while optimizing for energy efficiency.

Employing techniques like efficient highway merging were key strategies in their approach to making the most of each tank of fuel or battery charge. “For example, in heavy traffic on the highway, calculating the most optimal way to merge onto the highway without negatively affecting the energy efficiency of the vehicles already on the highway is crucial,” Gankov noted.

As NEXTCAR 2 enters its final year, the demonstration at the ARPA-E Summit served as a testament to the progress made in autonomous vehicle technology and its potential to dramatically improve energy efficiency in transportation.

• To Make Cars Safer, Connect Them to Everything ›
• The Scary Efficiency of Autonomous Intersections ›
• Fuel economy testing of autonomous vehicles - ScienceDirect ›

Willie Jones is an associate editor at IEEE Spectrum . In addition to editing and planning daily coverage, he manages several of Spectrum 's newsletters and contributes regularly to the monthly Big Picture section that appears in the print edition.

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The many, many missed deadlines for a fully autonomous vehicular future.

By Andrew J. Hawkins , transportation editor with 10+ years of experience who covers EVs, public transportation, and aviation. His work has appeared in The New York Daily News and City & State.

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In 2015, the then-lead of Google’s self-driving car project Chris Urmson said one of his goals in developing a fully driverless vehicle was to make sure that his 11-year-old son would never need a driver’s license. 

The subtext was that in five years, when Urmson’s son turned 16, self-driving cars would be so ubiquitous, and the technology would be so superior to human driving, that his teenage son would have no need nor desire to learn to drive himself. 

Well, it’s 2024, and Urmson’s son is now 20 years old. Any bets on whether he got that driver’s license? 

One of the hallmarks of the race to develop autonomous vehicles has been wildly optimistic predictions about when they’ll be ready for daily use. The landscape is positively littered with missed deadlines. 

In 2015, Baidu senior VP Wang Jing said the tech company would be selling self-driving cars to Chinese customers by 2020. In 2016, then-Lyft president John Zimmer claimed that “a majority” of the trips taking place on its ride-sharing network would be in fully driverless cars “within five years.” That same year, Business Insider said that 10 million autonomous vehicles would be on the road by 2020. 

GM said it would mass produce driverless cars without steering wheels or pedals by 2019. Ford, slightly more conservative, predicted it would do the same in 2021. And in a perfect encapsulation of mid-2010s autonomy hype, Intel in 2017 predicted a $7 trillion industry — more than double what the global auto industry does now — around autonomy by 2050. 

Of course, no one has been more bullish than Tesla CEO Elon Musk, who has turned making wrong predictions about the readiness of autonomous vehicles into an art form.

Of course, no one has been more bullish than Tesla CEO Elon Musk, who has turned making wrong predictions about the readiness of autonomous vehicles into an art form. “By the middle of next year, we’ll have over a million Tesla cars on the road with full self-driving hardware,” Musk said in 2019. Tesla’s Full Self-Driving (FSD) feature would be so reliable the driver could “go to sleep.” Teslas with the company’s FSD software are not autonomous, and drivers would be well advised to not sleep in their cars. 

Sure, there are some self-driving cars on the roads today. They’re in San Francisco, Phoenix, Los Angeles, Hamburg, and Beijing, among other cities. They’re operated by some of the biggest, most well-capitalized companies in the world. You can even ride in some of them.

But they’re stuck. Not stuck in the sense that a Tesla Cybertruck gets stuck in less than an inch of snow. But confined within geofenced service areas, held back by their own technological shortcomings, opposed by labor unions and supporters of more reliable modes of transportation, and restricted from driving on certain roads or in certain weather conditions. 

“The autonomous vehicle industry — particularly the companies developing and testing robotaxis — has gotten away for too long with selling a vision of the future that they should know perfectly well is never going to come to pass,” Sam Anthony, co-founder and CTO of Perceptive Automata, a now-defunct AV company, wrote in his newsletter in 2022.

We assumed the robots would be able to drive as freely as we do. After all, we built a world in which we humans can — and do — drive anywhere, all the time. So why did we get it so wrong?

Before we examine why the industry collectively whiffed the rollout of driverless cars, it’s instructive to look at why these predictions were made in the first place. Why set these goal posts if they never really mattered? 

Of course the answer is money. By promising that driverless cars were “just around the corner,” and on the cusp of taking over our roads, companies were able to rack in hundreds of billions of dollars to fund their experiments. 

The amount of money flowing into the autonomous vehicle space also had the knock-on effect of convincing regulators to take a lax approach when it comes to self-driving cars. AV boosters warned that too many rules would “stifle innovation” and jeopardize future gains, whether that was safety or job creation. 

And it turns out that regulators were very receptive to those arguments. The federal government — whether under Obama, Trump, or Biden — has done very little to stand in the way of companies testing their tech on public roads. A bill in Congress that would accelerate the rollout of cars with steering wheels and pedals has stalled over disagreements about liability, but you wouldn’t know it looking at these fundraising hauls. 

Some states, like California, have done their best to spin up some sort of regulatory playbook. But most were eager to attract companies under the belief that driverless cars were the future. And who wants to stand in the way of the future? 

For nearly a decade, AV operators were able to raise money almost without restriction.

For nearly a decade, AV operators were able to raise money almost without restriction. They did it through normal fundraising channels, or by tying themselves to big tech and car companies. Cruise Automation was acquired by General Motors. Ford invested $1 billion in Argo AI. Google, always slightly ahead of the rest, spun out its self-driving car project as Waymo. Amazon bought Zoox. Hyundai allied itself with Motional. Some have estimated over $160 billion has flowed into the industry over the past dozen or so years. 

And after the pandemic, the companies that weren’t able to cozy up to big automakers or tech giants found a new way to quickly raise cash: SPACs. Traditional IPOs were slow, and special acquisition companies were quick, so dozens of mobility-focused startups went public by merging with these so-called “blank check” companies in order to access more money faster. 

And despite a number of setbacks, like crashes and lawsuits and investigations, the cash kept coming in. It wasn’t until 2021, when the industry pulled in $12.5 billion led by GM’s Cruise raising a massive $2.75 billion, that funding for AV companies peaked. 

The predictions about the imminent arrival of safe, reliable self-driving technology helped speed the flow of money. And once those predictions failed to materialize, the money started to dry up. 

Why did the predictions fail? The technology, while incredibly effective at getting us most of the way there, stumbled as it got closer to the finish line. 

In the AV world, this is called the “long tail of 9s.” It’s the idea that you can get a vehicle that is 99.9 percent as good as a human driver, but you never actually get to 100 percent. And that’s because of edge cases, these unpredictable events that flummox even human drivers. 

When training an AI program on driving, you can predict a lot of what to expect, but you can’t predict everything. And when those edge cases eventually emerge, the car can make mistakes — sometimes with tragic consequences. 

When training an AI program on driving, you can predict a lot of what to expect, but you can’t predict everything.

Take the example of Cruise. In October of last year, a woman was hit by a human driver while crossing the street in San Francisco. The impact sent her flying into the path of a driverless Cruise vehicle, which immediately braked after also striking her. The Cruise vehicle then attempted to pull over to the side of the road, not realizing the woman was still trapped beneath the vehicle, injuring her further in the process. 

One of the first things Cruise did in the wake of the incident was to recall all 950 vehicles it had on the road in the US. This took the form of an over-the-air software update to the collision detection subsystem so the vehicle remains stationary during certain crash incidents, rather than pulling over to the side of the road. Cruise encountered an edge case, and it quickly issued a correction for it. 

But how many more edge cases are lurking in the shadows? And how many more people will be injured — or even killed — before these cars are seen as more reliable?

Waymo has been on the forefront of trying to convince the public and regulators that its vehicles are as safe, if not safer, than humans. It’s released a number of studies and statistical analyses in recent years that it says proves its vehicles get in fewer crashes, cause less damage, and improve overall safety on the roads. 

But for every Waymo, there’s an Elon Musk, whose misleading predictions about the imminent readiness of self-driving cars muddy the waters for everyone else who knows that the reality is much further away than previously thought. Waymo also assumes legal liability for crashes involving its vehicles — something Tesla has so far refused to do.

But Waymo isn’t driving the public’s perception of self-driving cars; Tesla is. Broken promises and failed predictions are what’s fueling the growing skepticism about self-driving cars in the public which, as the years plod by, gets more and more turned off by the idea of relinquishing control of their vehicles to a robot. 

Without passengers, there’s no business. But without safe, reliable technology, there’s no future for autonomous vehicles. 

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Hungary's Orbán meets Putin for talks in Moscow in a rare visit from a European leader

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MOSCOW (AP) — Russian President Vladimir Putin welcomed Hungarian Prime Minister Viktor Orbán to Moscow on Friday for a rare meeting with a European leader to discuss peace proposals for Ukraine.

Orbán's visit comes only days after he made a similar unannounced trip to Ukraine, where he met with President Volodymyr Zelenskyy and proposed that Ukraine consider agreeing to an immediate cease-fire with Russia.

“The number of countries that can talk to both warring sides is diminishing,” Orbán said. “Hungary is slowly becoming the only country in Europe that can speak to everyone.”

In comments at the beginning of their meeting that were televised, Putin suggested that Orbán had come to Moscow as a representative of the European Council, despite several European officials having condemned the visit.

The Hungarian prime minister, widely seen as having the warmest relations with Vladimir Putin among all European Union leaders , has routinely blocked, delayed or watered down EU efforts to assist Ukraine and impose sanctions on Moscow for its actions in Ukraine. He has long argued for a cessation of hostilities in Ukraine but without outlining what that might mean for the country's territorial integrity or future security.

That posture has frustrated Hungary’s EU and NATO allies, which have broadly considered Russia’s actions as a breach of international law and a threat to the security of countries in Eastern Europe.

The visit to Moscow was Orbán’s idea and was only agreed to on Wednesday, Putin’s spokesperson, Dmitry Peskov said, according to the Russian state news agency Tass.

Putin told Orbán that he wanted to hear about the position of his “European colleagues” on Ukraine and discuss Moscow's plan for peace as well as bilateral relations. Orbán noted that the meeting was the fourteenth time the leaders have held bilateral talks since 2009.

European officials and leaders have heavily criticized Orbán's visit to Moscow, something only one other European leader has done since the start of the conflict.

“This is about appeasement. It’s not about peace,” European Commission spokesperson Eric Mamer said.

Hungary at the beginning of the month took over the six-month rotating presidency of the EU Council, a largely formal role that can be used to shape the bloc’s policy agenda. Orbán has said he wants to use the presidency to advocate for an end to the fighting in Ukraine.

The EU’s foreign policy chief, Josep Borrell, said in a statement that Orbán’s visit to Moscow “takes place, exclusively, in the framework of the bilateral relations between Hungary and Russia.”

“Prime Minister Orbán has not received any mandate from the EU Council to visit Moscow," Borrell said, adding that his "position excludes official contacts between the EU and President Putin. The Hungarian Prime Minister is thus not representing the EU in any form.”

He noted that Putin has been indicted by the International Criminal Court and an arrest warrant released for his role in relation to the forced deportation of children from Ukraine to Russia.

Kaja Kallas, the outgoing Estonian prime minister nominated to become the next EU foreign policy chief, accused Orbán of “exploiting" the presidency and said the Hungarian leader is trying “to sow confusion.”

"The EU is united, clearly behind Ukraine and against Russian aggression,” Kallas, a staunch supporter of Ukraine, wrote Friday on the social media platform X.

In an email on Friday, Orbán's press chief, Bertalan Havasi, said the Hungarian leader's trip comes “as part of his peace mission” — reflecting the image of a peacemaker that the populist leader has cultivated since Putin sent his troops into Ukraine in February 2022. Hungarian Foreign Minister Péter Szijjártó also joined the trip to Moscow, according to a post on his Facebook page.

Speaking to Hungarian state radio before departing Friday morning, Orbán said he was aware that his country’s presidency of the EU “does not entitle us to negotiate on behalf of anyone.”

Without mentioning reports of his planned trip to Moscow, he said his trip earlier this week to Kyiv did not “need a mandate, because I do not represent anything. The only thing I do is go to those places where there is a war or the threat of war that has negative consequences for Europe and Hungary as well.”

Other EU officials have expressed dismay that Orbán undertook the trip unilaterally and without the approval of the bloc’s member states.

European Council President Charles Michel on Thursday said on the social media platform X that “the EU rotating presidency has no mandate to engage with Russia on behalf of the EU.”

“The European Council is clear: Russia is the aggressor, Ukraine is the victim. No discussions about Ukraine can take place without Ukraine,” Michel wrote.

Orbán is the first European leader to visit Russia and sit down for talks with Putin since Austrian Chancellor Karl Nehammer visited Moscow in April 2022, just weeks after Putin ordered his troops into Ukraine. During that visit he said he raised the issue of crimes allegedly committed in Ukraine by Russian forces.

Spike reported from Budapest, Hungary. Associated Press reporters Dasha Litvinova in Tallinn, Estonia, and Emma Burrows in London contributed to this report.