AI In Autonomous Vehicle Technology

In a recent post on X, Malcolm Gladwell raised an incredibly important—yet often under-discussed—limitation of driverless cars operating in urban environments. Without changes in how humans and robots interact on the road, pedestrians, human drivers, and other road users may increasingly exploit the conservative, safety-first behavior of autonomous vehicles. Picture a busy, unsignalized crosswalk on a crowded city street. Human drivers often inch forward, subtly asserting themselves to create space—imperfect, but effective. A driverless vehicle, in contrast, may be programmed to wait patiently, prioritizing safety above even the slightest risk. Sure, we could get used to longer travel times, redesign the infrastructure (e.g., add more traffic signals or physical barriers), but those changes will take years—and require public investment that has yet to materialize or even be adequately discussed. As my 2018 TEDx highlights, "There's more to the safety of driverless cars than artificial intelligence." This isn’t just a technological issue—it’s a human one. See comments for links.

Imagine teaching a robot to drive a car. You'd want it to understand what's really important, like staying in its lane by using the road lines, not just following bushes on the side. Massachusetts Institute of Technology researchers have found a way to make AI smarter in this way! Here's the cool part: They developed a special type of AI, called Neural Circuit Policies (NCPs), which thinks more like a human. It's like teaching a student to understand math problems, not just memorize answers. These NCPs actually learn the cause-and-effect behind their actions. Analogy time! Think about cooking. A regular recipe-following robot might cook only by following steps exactly as written, relying on timers and measurements without understanding why they’re used. The NCP approach would be like a chef who knows why each step is needed (like why we let the dough rise) and adapts if an ingredient changes or if things go awry. In tests, these smart AIs navigated drones through tricky environments, like forests or foggy weather, and performed like pros. This means they can make better judgments in unpredictable conditions, just like how you'd adapt your driving if it started raining heavily during a clear day. Supported by leaders like the United States Air Force and Boeing, this research could revolutionize how we use AI in cars, planes, drones and really any domain. By understanding the task truly, these AI systems are set to be more reliable and versatile in the real world. #SmartAI #Innovation #AIForGood #MIT #TechRevolution #UnderstandingAI #NextGenTech #USAirForce #MITCSAIL

🎇 On National Day, I went for a leisurely drive in San Francisco and ended up "stress-testing" a Waymo self-driving car on the road. 🚗 As an autonomous driving practitioner, who wouldn't be curious about the real-time robustness of the cutting-edge Waymo One driving system? While cruising downtown, I accidentally noticed a Waymo car tailgating me. While this isn't unusual for SF citizens, a wild, "evil" idea suddenly hit me: Why not directly "adversarially attack" the world's autonomous driving status quo? I executed an unexpected maneuver—suddenly reversing—to see how it would react. 🌟 The response was stellar! At the moment I reversed, Waymo One honked instantly—quicker than any human could—activated its hazard lights, and backed away to maintain a safe distance. This reckless move on my part served as an edge case to test the algorithm's robustness under extreme conditions and, potentially, could be a challenging training sample to enhance Waymo's future autonomous systems. 💫 Thrilled to personally experience how current cutting-edge autonomous algorithms handle rare driving behaviors—and how stable and safe Level 4 autonomy is in dealing with diverse scenarios. However, it also prompted deep reflection as an AI researcher in this field: 🤔 In an industry with little room for error, how can we ultimately avoid or minimize issues that AI fails to handle? 💡 I believe two research directions are particularly promising for achieving Level 5 level autonomy in future mobility systems: - 1️⃣ Development and deployment of vehicle-to-everything (V2X) cooperative systems (including V2V, V2I, V2P, etc). Our initial studies (e.g., V2X-ViT, ECCV'2022 arxiv.org/abs/2203.10638) show that in scenarios with severe occlusions or noise, such cooperative systems can significantly enhance the robustness of perception systems, thereby eventually improving traffic safety. - 2️⃣ Adversarial scenarios generation (including Sim-to-Real, generative modeling). Research done by my colleagues at UCLA (V2XP-ASG, ICRA'2023 https://lnkd.in/gu5nKVHD) shows that adversarial learning techniques can effectively simulate adversarial scenarios, greatly improving model robustness in complex "corner case" situations. Of course, it's often infeasible to collect such collision scenarios. 👨🏫 As a new Assistant Professor of CS at Texas A&M University, I will lead a group focusing on these exciting research directions, which can be a proactive approach to reducing accidents and improving safety for all. 🔥 I look forward to future collaborations with governments, academics, and companies to research and develop data and algorithms that can help enhance the safety of vulnerable road users, especially seniors and children. We envision a people-centered intelligent transportation system in the future. Interested in these topics? Let's connect and discuss further! #AutonomousVehicles #AI #MachineLearning #SmartCities #Transporation #Humanity #Mobility

What happens if a driver becomes unresponsive while on the road? It’s a scenario none of us want to imagine, but it’s one that modern technology is beginning to address. Take Volkswagen’s Emergency Assist, for example - a system designed to step in during critical moments when a driver can’t respond. How Does It Work? Emergency Assist uses monitoring systems to detect inactivity from the driver. If it senses something’s wrong, it takes a series of actions to ensure safety: - Stays in the Lane: Keeps the vehicle steady within its lane to avoid accidents. - Signals Other Drivers: Activates hazard lights to warn nearby vehicles. - Attempts to Alert the Driver: Uses sounds, seatbelt vibrations, or gentle braking to try to regain attention. - Moves to the Shoulder: If the driver remains unresponsive, the system safely steers the car toward the roadside. - Stops the Car: Finally, it brings the vehicle to a safe stop out of traffic. Emergency features like this are stepping stones toward fully autonomous cars, which promise even greater advancements in road safety. Companies like Waymo are leading the charge, and the data speaks volumes: - 70% fewer injury-related crashes compared to human-driven vehicles. - 6x less likely to be involved in severe accidents requiring airbag deployment. - Notably, none of the serious incidents in Waymo’s tests were caused by the self-driving system itself. In 23 serious cases involving Waymo cars: - 16 were rear-end collisions caused by other drivers. - 3 were due to red-light violations by other vehicles. - 0 involved the Waymo system making critical mistakes like running a red light. The road to full automation will take time, but these show incredible potential for reducing accidents and making driving less stressful. How do you feel about cars becoming more autonomous? Do these safety innovations make you feel more confident about the future of driving? #innovation #technology #future #management #startups

🚗 🚗 🚗 Data Blind Spots and Their Impact in Automotives Visual AI 🚗 🚗 🚗 Excited to share my latest article in WardsAuto on the critical challenge of achieving 99.999% accuracy in automotive visual AI (link below). Drawing from both my academic research at University of Michigan College of Engineering and industry experience with Voxel51, I explore why data blind spots are the main obstacle holding back autonomous vehicle development. Data blind spots --- critical gaps in datasets that happen in practice but are out of domain for the model --- prevent AI models from achieving the level of robustness necessary to deliver real systems to production. I bring this to light with a real case study involving phantom potholes and how unified data visibility helped solve it. The key - bringing in teams, data, and models together to rapidly identify and fix issues. Read the full piece to learn how we can cut the 80% AI project failure rate and make road transportation safer: https://lnkd.in/df8Gf-xd

Autonomous vehicles spent years in pilot mode—never quite arriving. But if you ask a San Franciscan in 2025, that’s all changed. In SF, Waymo now accounts for 27% of rideshare bookings (!!), surpassing Lyft to become the second-largest operator in the market (per the below Bond Cap report). For public safety leaders in other big cities, this shift signals both opportunity and responsibility. AVs seem to deliver better safety outcomes over millions of miles driven—fewer crashes, fewer injuries per some studies—but also challenge how cities think about traffic enforcement, infrastructure, and emergency response in a world with fewer drivers. From an emergency management perspective, partnering with AV fleet deployers could unlock new capabilities: dynamic rerouting to clear lanes or areas for emergency vehicles, real-time passenger notifications, and coordinated evacuation at fleet scale. Yet city-scale driverless fleets also introduce an expanded set of vulnerabilities—and we can’t afford to be reactive. Proactive engagement and planning will be key to helping AVs support, rather than undermine, urban safety and resilience. Link to source: https://lnkd.in/eiPSSG-f

Elon Musk is building the endgame for human drivers. Tesla isn't just creating a robotaxi service. They're building the API layer that will control how cities move. A foundational infrastructure that every smart service will need to integrate with. Each vehicle becomes a node in a real-time platform: collecting data, reacting to algorithms, and reshaping urban flow. Tesla's launch locations aren't random. They reflect how the API economy is redefining physical systems. Different laws mean different data integrations, turning compliance into a competitive advantage. Each robotaxi becomes a roaming sensor tracking traffic, analyzing infrastructure, and decoding cities. Tesla isn't building a taxi company. They're architecting the digital nervous system of tomorrow's cities and positioning themselves as the gatekeepers for anything that moves. At scale: • Parking becomes algorithmic • Fleets self-position automatically • Traffic flow becomes programmable Picture this: • Ambulances routing through Tesla's API • Delivery fleets scheduled by traffic algorithms • Fire trucks requesting lane access via platform protocols When urban infrastructure syncs to Tesla's platform, control shifts to whoever owns the API. APIs are becoming the governance layer for physical systems. Banking, healthcare, logistics: they're next. This shift defines who governs tomorrow's infrastructure. Not just technologists, but architects of digital governance. People who see APIs not as code, but as tools of power. Infrastructure isn't just concrete anymore. It's computation. - Thanks for reading! I'm Baptiste Parravicini: • Tech entrepreneur & API visionary • Co-founder of APIdays, the world's leading API conference • Passionate about AI integration & tech for the greater good Curious about what it takes to lead in the age of digital infrastructure? Check the comments ⬇️