Rivian Unveils Custom Silicon, R2 Lidar Roadmap, and Universal Hands Free

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

The three most prevalent themes in the Hacker News discussion are:

Skepticism Regarding the Business Viability and Rationale for Custom Silicon Development (Especially for Cash-Burning Companies like Rivian): Many users questioned why a company like Rivian, which is financially struggling, would invest heavily in designing its own chips rather than using established Commercial Off-The-Shelf (COTS) options from companies like NVIDIA. The high cost and long lead time of custom silicon development were cited as major risks.
- Supporting Quote: "For a company that's bleeding cash aggressively, with a short runway, to engage in this seems iffy." ("potatolicious")
- Supporting Quote: "I'm very optimistic about automated cars being better than most humans but wonder about side effects." ("stoneman24") (This is in reference to the high cost and risk associated with developing custom solutions.)
Intense Debate Over the Necessity and Safety of Automotive LiDAR Technology: A significant portion of the thread devolved into a technical discussion about LiDAR, particularly concerning its safety relative to human eyes and phone camera sensors, and whether COTS silicon (like NVIDIA's Orin/Thor) or custom solutions were better suited for automotive requirements (especially harsh environments).
- Supporting Quote: "What COTS equipment is there that has all the CPU/Tensor horsepower these systems need... AND is reasonably power efficient AND is rated for a vehicle (wild temp extremes... constant vibration...)? Yea, Tesla has some. But they aren't sharing their secret sauce. You can't just throw a desktop computer in a car and expect it to survive for the duration." ("ShakataGaNai")
- Supporting Quote: "The reason for why your phone camera would get damaged, but not your eyes, is due to the nature of how camera lenses work. They are designed to gather as much light as possible from a direction and focus it onto a flat, tiny sensor." ("filoleg")
Consumer Dissatisfaction with Current Automotive Software, Especially Infotainment Features: Users expressed frustration that high-end vehicles are often "nerfed by bad software," frequently citing lack of standard features like CarPlay/Android Auto as a key reason for choosing competitors.
- Supporting Quote: "I would be ecstatic to see the entire industry wiped out by a newcomer on the scene." ("hateselfdriving") (This sentiment followed complaints about complex, proprietary systems.)
- Supporting Quote: "Meanwhile, the only thing people really want from Rivian is CarPlay / Android Auto support, lol." ("nicksergeant")
- Supporting Quote: "I can't imagine how bad it must've been on their own stack that they gave up and bought this other seemingly broken stack" ("igor47") (Regarding Rivian's software reputation contrasting with VW's massive payment for it.)

🚀 Project Ideas

Automotive Software Bug Triage & CI/CD Simulator

Summary

A specialized platform designed for automotive software teams to model and diagnose complex, state-dependent "heisenbugs" (like doors refusing to open or controls causing unexpected behavior) common in highly integrated vehicle firmware.
Core value proposition: Provide a standardized simulation environment that mimics hostile automotive conditions (EMF, state machines, asynchronous communication delays) to reliably reproduce and patch mission-critical bugs before over-the-air (OTA) deployment.

Details

Key	Value
Target Audience	Embedded systems engineers and QA teams working on vehicle firmware (OEMs or Tier 1 suppliers).
Core Feature	State Machine Visualization and Replay: Allows users to input logs of failure conditions and replay the exact sequence of internal state transitions, network interruptions, and hardware responses to isolate race conditions.
Tech Stack	Simulation: Custom C++/Rust environments interfacing with formalized state machine languages (like SCXML). Visualization: Web-based dashboard using D3.js or similar for real-time visualization of state graphs.
Difficulty	High (Requires deep understanding of real-time operating systems and automotive bus protocols like CAN/Ethernet).

Notes

Why HN commenters would love it: Directly targets the severe architectural concerns raised: "I would assume a badly flawed architecture or non existent technical leadership" due to the kinds of bugs described in the Rivian discussion.
Potential for discussion or practical utility in improving the quality assurance pipeline for safety-critical, complex embedded systems.

Infotainment Mirroring Service (IMS)

Summary

A cross-platform service that provides reliable, feature-rich integration for mobile operating systems (like iOS/Android) onto vehicle infotainment screens, specifically addressing the lack of native CarPlay/Android Auto support in newer vehicles like Rivian.
Core value proposition: Dramatically improve the usability and user experience (UX) of vehicles lacking standard smartphone integration by securely "mirroring" essential app functions onto the native display.

Details

Key	Value
Target Audience	Owners of premium/new vehicles (like Rivian, some legacy OEM models) whose manufacturers omit or provide poor implementation of Apple CarPlay/Android Auto.
Core Feature	Secure, low-latency mirroring and controlled integration of selected mobile apps (messaging, navigation via Waze, third-party audio apps) onto the vehicle's high-resolution native display via a secure bridge/dongle.
Tech Stack	Backend: Node.js/Python for server management/device bridging protocols. Frontend/Client: React Native (for app integration layer), low-level OS interface using secure bootloaders/virtual display APIs on the car side (if possible via external hardware dongle) or cloud streaming if direct integration is blocked.
Difficulty	High (due to automotive security protocols and OEM resistance, likely requires robust hardware pairing.)

Notes

Why HN commenters would love it: Addresses the direct frustration: "Meanwhile, the only thing people really want from Rivian is CarPlay / Android Auto support, lol." and "It's maddening that $100k purchases get totally nerfed by bad software."
Potential for discussion or practical utility by creating a viable, aftermarket solution for drivers who prioritize familiar, modern smartphone ecosystems over bespoke, often buggy OEM UX.

Customized Lidar Signal Safety Analysis Tool

Summary

A diagnostic and simulation service that allows automotive/LIDAR manufacturers to upload specifications (wavelength, pulse power, scan patterns) of their proposed LIDAR units to model potential eye and camera damage risks across various operating conditions.
Core value proposition: Quantify the actual risk profile (Class 1 compliance, camera degradation) across the entire product lifecycle, specifically addressing ambiguity surrounding power/wavelength interactions debated by users (e.g., 1550nm vs 905nm cumulative exposure).

Details

Key	Value
Target Audience	Automotive sensor engineering teams and regulatory compliance officers preparing new ADAS systems.
Core Feature	Cumulative Intensity Modeling: Simulates the integrated energy delivered to a human eye or CMOS sensor over time, accounting for factors like beam divergence, pedestrian movement, and high densities of co-located sensors (e.g., on busy urban roads).
Tech Stack	Backend: Scientific computing libraries (NumPy/SciPy in Python) for physical modeling. Database: Graph database (Neo4j) to map component specs to regulatory standards and historical incident reports.
Difficulty	Medium/High (Requires accurate physics modeling, but the interface and utilization can be productized).

Notes

Why HN commenters would love it: It addresses the sophisticated, multi-faceted safety debate around Lidar: "If you have many lidars around, the beams from each 905 nm lidar will be focused to a different spot on your retina..." and provides a platform to settle arguments about "eye safe" vs "camera safe."
Potential for discussion or practical utility by offering a concrete service to manage regulatory uncertainty surrounding complex sensor interactions.