Part 1 was Tesla vs Waymo, and by the end of it I'd landed on Tesla, mostly because of how the two scale. But somewhere in my own head after publishing that, I realised I'd boxed myself into a pretty small world. Two companies, one country. The second I started looking outside the US, the whole comparison fell apart and had to be rebuilt from scratch.
Turns out China isn't picking a side in the vision-vs-lidar debate at all. They're doing both, aggressively, and building an entire road network to back it up. And then there's India, which doesn't fit into either camp because the roads themselves refuse to cooperate with any of this.
So this part is the "everyone else" part.
Tesla bets on cameras. Waymo bets on cameras plus lidar plus a fully mapped world. China's mainstream approach sits in between those two, leaning hard into what's called multi-sensor fusion, cameras, lidar, and radar together, run through end-to-end neural networks. Not a compromise exactly. More like refusing to accept the premise that you have to pick one.
A few names came up over and over while I was reading through this.
Baidu Apollo is the closest thing China has to a Waymo. Apollo Go runs as a fully driverless L4 robotaxi service in over ten cities, Wuhan and Beijing among them, and their newest robotaxi, the RT6, carries 38 sensors. Thirty-eight. Compare that to Waymo's 23 and you start to see the philosophy, redundancy on redundancy, because if one sensor gets confused, three others are still watching.
Huawei doesn't even make cars, they license the tech to companies like BAIC and Changan. Their system, ADS, runs on what they call a "World Action Model" alongside a general obstacle detection network, trained specifically on virtual edge cases. Which is a genuinely smart move when you think about it, you can't wait around for a real cow to wander into traffic to teach your model what a cow wandering into traffic looks like, so you simulate it instead.
XPeng is the one that actually surprised me. Their high-end cars run sensor fusion, two lidars, five radars, two dozen cameras, feeding into a Vision-Language-Action model they call X-Mind. But their budget model, the Mona M03, strips the lidar out entirely and goes pure camera. That's Tesla's whole philosophy, just imported into a fifteen-thousand-dollar car. Same company, two completely different bets depending on the price point.
BYD does this even more explicitly with their "God's Eye" system, split into three tiers. Tier A gets three lidars for the luxury Yangwang U8. Tier C, on a car that costs about ten thousand dollars, is pure vision, no lidar at all. So the debate I spent all of part 1 treating as an either/or is actually something BYD just solves by tier. Rich people get redundancy, everyone else gets cameras. Practical, even if it's a little uncomfortable to say out loud.
Here's something I hadn't really considered before this. HD maps are expensive and slow to update, and China's biggest players, Huawei, XPeng, Li Auto, are moving toward "mapless" driving instead, where the car just reads the road live instead of relying on a pre-built map of it. This solves Waymo's actual weak point, the whole geofencing thing, needing a city to be mapped down to the centimetre before the car can drive there at all.
But mapless still leaves the camera-blindspot problem sitting there, unsolved. That's where V2X comes in, and it's honestly the part of this research that made me stop and think the longest.
The idea is simple enough on paper: instead of only making the car smarter, make the road smarter too. Cameras mounted on street poles feeding perception data straight into the car. Traffic lights talking to vehicles over 5G. A car that can't see around a truck getting the missing picture handed to it by the infrastructure instead of guessing.
There's one example that stuck with me. A construction site, red light, but a worker waving traffic through anyway, the kind of situation a purely visual AI genuinely struggles to parse because the rules and the reality are contradicting each other. With V2X, that worker could carry a chip that overrides the signal directly for the car. Could. Not does, not yet. Which is the catch with basically all of this, it's a real solution to a real problem, sitting entirely in the future tense.
And that future tense matters more the further you get from China. This kind of coordinated infrastructure only really works if a government can mandate it across an entire country at once. The US can't do that cleanly, every state sets its own rules. Realistically, most of the world can't either. Smart traffic lights sound great until you ask who's paying to install them in a city that still has potholes nobody's filled in a decade.
If you actually zoom out to markets where both companies compete freely, Europe, Southeast Asia, South America, the story flips almost entirely. BYD dethroned Tesla as the world's top-selling EV maker in 2024, 4.27 million vehicles against Tesla's 1.78 million, and has since started outselling Tesla monthly even in the UK.
The reason comes down to something I didn't expect going in: BYD builds almost everything itself. Battery, chips, motors, all in-house, which they estimate gives a car like the Seal something like a $4,700 structural cost edge over a comparable Model 3. And "God's Eye" ships free on most of their lineup, while Tesla still charges $8,000 upfront for FSD.
The engineering philosophies genuinely don't match up. Tesla is software-first and deliberately minimal, one neural network taking raw pixels straight to steering output, trained on their own Dojo supercomputer. BYD goes the opposite way, wiring the AI directly into the physical chassis through their DiSus body control system. Their cars can read the road ahead with lidar and adjust the suspension before hitting a bump, or independently torque each wheel during a blowout to keep the car stable on three wheels. Tesla is trying to make the brain smarter. BYD is trying to make the whole body smarter, brain included.
None of that means BYD's software is actually good, though. Owners have reported phantom braking, ghost steering that jerks the car toward oncoming lanes, a car suddenly accelerating to 93 km/h in a 60 zone. Free doesn't mean finished. And BYD makes about $1,400 profit per car versus Tesla's $4,583, which tells you the free-software strategy is buying market share at the direct cost of margin. Whether that trade is worth it long-term, I genuinely don't know. Ask again in five years.
Worth saying too, none of this is reaching the US any time soon. BYD's effectively locked out by 247.5% tariffs, and the EU just added another 17% on top of an existing 10%, partly over the same data-privacy suspicion that follows every Chinese-made camera-and-sensor product right now.
Everything above assumes a road that mostly behaves. Lane markings people follow, a predictable set of other vehicles, rules that get broken occasionally rather than as a baseline condition. India doesn't offer any of that, and that's not a knock, it's just structurally what the roads are.
This is where Swaayatt Robotics comes in, and honestly their whole approach reframes the problem instead of trying to solve it head-on. Their system treats the entire driving environment as one continuous corner case. No waiting for a gap to safely appear, because on an Indian road it often just doesn't. Instead the car negotiates, adversarial multi-agent negotiation is the actual term they use, which is a very academic way of describing what basically amounts to nosing into space the way you'd have to as a human driver here.
If a cow or a rickshaw or an oncoming car doesn't yield, the system changes its plan stochastically, meaning probabilistically, not according to some fixed rulebook. Swaayatt calls the resulting behaviour passive-aggressive navigation, and reading that phrase for the first time actually made me laugh, because it's such an accurate description of how you have to drive in Bangalore. Not reckless. Just refusing to freeze up and wait for permission that was never going to come.
Full self-driving in Indian cities, at least any time soon, still feels like a stretch to me even after reading all this. But ADAS, the assisted layer rather than the fully autonomous one, feels far more realistic as the actual next step here. And there's a data problem underneath all of it that I keep circling back to: Waymo's data is fully unsupervised, real self-driving, while Tesla's and the Chinese systems are still supervised, a human present the whole time. So even setting the chaos of Indian roads aside, we genuinely can't compare apples to apples on data quality alone, let alone once you throw in traffic that doesn't follow anyone's rules to begin with.
Which is basically the exact question I couldn't stop thinking about after all this reading. If passive-aggressive negotiation is what makes a human survive Indian traffic, can you actually get a reinforcement learning agent to learn that same behaviour from scratch? Not hardcode it in, but have it genuinely arrive there on its own, the way a human does after enough time behind the wheel.
That's part 3. And that one isn't research anymore, that's me actually trying to build it.