Will We Ever Truly Go Driverless? Future of Self-Driving Cars

When we remove the hype and examine exactly what stage this technology is currently at, where do we stand in the Future of Self-Driving Cars? While it is true that vehicles that are fully autonomous have not been made available for public use, these systems have advanced significantly using artificial intelligence (AI), machine learning, and sensor technology breakthroughs.

The more sophisticated examples employ sets of cameras

Radar and lidar provide continuous 360-degree vision around them while driving on roads or highways without human intervention. In addition to people walking bicycles along sidewalks, there may be other obstructions outside such as road signs; these need to be identified too!

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The Reality of Autonomy Today

By combining this panoramic environmental awareness with high-definition mapping data and smart AI driving systems, these autonomous test vehicles have proven capable of self-navigating through an astounding array of scenarios and conditions.

This advancement in Science & Technology has allowed these vehicles to log millions upon millions of real-world miles operating in autonomous mode, successfully piloting themselves through dense urban centers, heavy traffic, harsh weather conditions like snow and fog, and even complex detour situations like construction zones.

From the outside looking in

Based on all the jaw-dropping video evidence we’ve been shown, it seems these self-driving cars have essentially cracked the code on autonomy and are practically ready for consumer prime-time. But dig a little deeper, and it quickly becomes clear that key challenges and risks still remain serious roadblocks.

Despite the futuristic advancements

Automakers and tech companies remain fully reliant on fallback operators – teams of trained vehicle operators who must remain ever-vigilant behind the wheel, ready to take over when the self-driving system inevitably encounters an outlier scenario it simply cannot rationally navigate on its own. These human safety drivers are still very much a necessity, frequently needing to wrest control back from the autonomous mode when things go awry or the AI gets flummoxed by certain unforeseen circumstances.

In other words

While self-driving tech can handle the reasonable stuff reasonably well, the big lingering challenges lay in those pesky edge cases and outlier scenarios involving bike messengers whipping between lanes, pedestrians behaving erratically, temporarily obstructed road signage, and other unpredictable real-world complexities.

Self-driving systems

Current self-driving systems still falter frequently on these types of curveballs, failing to properly detect, contextualize, or project intelligent responses that a human driver could easily handle.

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As such, while we’ve gotten tantalizing glimpses of the autonomous future

We’re still likely quite a few years away from clearing the hefty remaining roadblocks to consumer-ready self-driving vehicles entering full production.

The Technical Roadblocks Ahead

Achieving reliable Level 5 full autonomy, where no human intervention is required whatsoever, involves solving some gnarly lingering technical puzzles spanning the realms of sensor hardware, AI software, mapping data, and rigorous real-world validation:

Sensing Limitations

The visual sensors enabling self-driving perception, no matter how advanced, still face obstacles in reliably classifying objects and scenarios with high fidelity. Cameras, radar, and lidar can easily become obstructed or generate erroneous data in suboptimal conditions like heavy rain, snow, fog, or if sensors get damaged. Developing next-gen sensors that deliver higher resiliency and fidelity remains a pressing need.

Software Struggles

While modern AI vision systems excel at narrow tasks like image classification, today’s self-driving software and prediction models still falter in reliably detecting, tracking, and anticipating the behaviors of pedestrians, cyclists, animals, and unfamiliar moving objects in the path of travel. These “edge cases” routinely stump the AI as each can play out in unpredictable, context-specific ways. Simply coding for every possible scenario and outcome is an arduous Mount Everest to climb.

Environmental Understanding

Beyond just perceiving individual objects, self-driving cars need higher-order skills to contextualize and understand the entire environment around them – from intersections, traffic signals, crosswalks, and lane guidance to more nuanced elements like construction zones, police officer hand signals, debris in the road, and virtually any other factors humans might encounter during driving. Only with this holistic environmental intelligence can autonomous cars properly navigate and project any potential hazards.

Validation & Testing

No company wants to be the next Uber, unleashing a beta self-driving system into the world that ends up tragically injuring or killing someone. As such, even if engineers crack the above technical puzzles, the final hurdle involves exhaustively validating autonomy systems as safe through meticulous real-world testing spanning infinite variations of conditions and edge cases. This data-driven validation process to statistically prove reliability and safety is likely to take years upon years of in-the-field road testing and analysis.

Cybersecurity Risks

Autonomous vehicles controlled predominantly by software and swarming with droves of sensor data will inevitably be tasty targets for malicious hackers looking to cause chaos. Ensuring these AI driving systems have bulletproof digital security alongside built-in secure failover systems ready to safely hand control back to humans in the event of hacks or systems failures looms as another pressing priority.

The Ethical Conundrums

But even if engineers are able to solve the riddle of full technical autonomy. Releasing these self-driving cars into the wild reveals a whole separate tangle of thorny. Ethical and philosophical conundrums we have yet to collectively unravel. At their core, these intellectual knots speak to the immense challenges of encoding human morals. Values, and ethical decision-making frameworks into cold computer systems ill-equipped for ambiguity.

The classic thought experiment illustrating the core dilemma is that of the Trolley Problem:

Imagine an autonomous car barrelling towards a crowd of pedestrians in its path. But it has the ability to swerve into a concrete barrier. Severely injuring its own passenger. What’s the ethical decision in this lose-lose scenario? Minimize casualties by endangering the passenger, or protect the passenger at all costs?

AI System

Reasonable people might argue persuasively for either option. But for self-driving cars, their AI systems will need to be programmed. Make such split-second decisions adhering to predefined ethical principles. Game out the random scenario outcomes. And we quickly spiral into a bramble patch of moral thickets over issues like entity discrimination. (should robots assign different values to passenger life vs. pedestrian life?). Property considerations, obeying rules of the road at all costs versus situational flexibility, and so on.

Self Driving System

While worthwhile efforts have arisen to crowdsource and standardize the moral guidelines we’d want self-driving vehicles to follow. There’s a larger question of whether we should cede the weighty matters. Ethical reasoning to algorithms in the first place. It’s simple enough for autonomous cars to be instructed to minimize casualties in the aggregate. But what if the system is later revealed to be inadvertent? Weighing certain demographics as more or less valuable in its ethical calculus?

Simply put, human drivers already have an innate

If flawed, the ethical code shapes our in-the-moment driving decisions. That innate system of morals and principles we all carry around in our grey mush. Informed by background, culture, experience, empathy, and the butterfly effects of thousands of little everyday. Choices cannot simply be uploaded into an AI’s neural networks, at least in its current paradigm.

There’s a strong case that for autonomous vehicles to truly earn society’s trust. Those inevitable ethically fraught edge cases need to be handed back to human judgment and accountability. Rather than delegated to an algorithm’s moral model of the world.

Looking Ahead Down the Road

Even before we resolve these enormous technical and ethical potholes. Self-driving vehicles remain on course for some form of gradual, geofenced arrival over the next few years. Don’t expect driverless cars for individual ownership and operation anytime soon. But the field is ripe for deployment in relatively controlled mobility services and applications first. Think autonomous taxi fleets, delivery services, and transit in contained lower-speed campuses or retirement communities.

In these constrained domains with simplified rules and operating parameters. self-driving cars can prove their mettle while developers. Focus on expanding capabilities into more complex environments over time. Once the safety and reliability metrics are indisputably crossed and public trust attained. We’ll then likely see autonomous vehicles begin gaining wider

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