Research task (AI & ML):
Write a concise technical report exploring why some autonomous vehicle systems prioritize LiDAR while others rely on neural networks and cameras.

Evaluation rubric:
criterion: The provided technical report is concise and does not approach the length of a scholarly article.
weight: 5
axis: Explicit Criteria

criterion: The introduction explicitly states the report's objective: comparing LiDAR versus neural net systems selection for autonomous vehicles.
weight: 5
axis: Communication Quality

criterion: The response conclusion synthesizes the key trade-offs into a forward-looking analysis of industry convergence, rather than repeating earlier content.
weight: 4
axis: Synthesis of Information

criterion: The response uses technical language correctly and defines all acronyms (e.g., LiDAR: Light Detection and Ranging, CNN: convolutional neural network, FMWCW: Frequency-Modulated Continuous-Wave Radar, ODD: Operational Design Domains, etc.) upon first use.
weight: 5
axis: Implicit Criteria

criterion: The response contrasts the technical trade-offs between different LiDAR variants (e.g., spinning ToF vs solid-state MEMS/OPA, flash ToF vs scanning, 905 nm pulsed vs 1550 nm (ToF/FMCW), Geiger-mode SPAD vs linear-mode APD).
weight: 4
axis: Implicit Criteria

criterion: Explains the specific mechanism of monocular depth estimation, including its reliance on learned visual cues (e.g., relative object size, perspective, texture gradients, contour continuity).
weight: 4
axis: Implicit Criteria

criterion: The response explains the architectural trade-offs between "early fusion" and "late fusion" strategies, detailing how each impacts computational load and perception accuracy (i.e., early fusion is computationally heavier upfront due to merged modalities and more accurate in high-detail scenery, late fusion is more modular and accurate in scenery when inter-modal relationships are not critical).
weight: 2
axis: Implicit Criteria

criterion: The response discusses trade-offs in latency
weight: 5
axis: Implicit Criteria

criterion: The response details specific algorithmic mitigation strategies for adverse weather (e.g., AI-based "de-weathering" for cameras, the use of 1550 nm wavelength lasers for LiDAR).
weight: 4
axis: Implicit Criteria

criterion: Considers redundancy and fail-safe behaviors in perception systems of both LiDAR and camera with neural networks, and for the latter, links such failures with generalization limitations.
weight: 4
axis: Synthesis of Information

criterion: The response provides a case study or describes an AV manufacturer example (e.g., Tesla, Waymo, DJI, GM Cruise)
weight: 2
axis: Implicit Criteria

criterion: The response includes at least one mathematical model for LiDAR and cameras with neural networks (e.g., the LiDAR range equation, convolutional layer, brightness constancy, fusion architecture).  
weight: 3
axis: Implicit Criteria

criterion: The response describes key perception algorithms and formalisms (e.g., SLAM, semantic segmentation, depth estimation; 2D vs. 3D) used by LiDAR and camera-based neural systems.
weight: 3
axis: Implicit Criteria

criterion: The report quantitatively contrasts the performance of a specific, named LiDAR-based model with a camera-only model on the same perception task (e.g., 3D object detection, reconstruction, depth perception, lane detection), using published results from a named benchmark (e.g., nuScenes, A2D2, Waymo open dataset, PandaSet).
weight: 3
axis: Implicit Criteria

criterion: The response includes discussion regarding generalization limitations in deep learning (e.g., domain shifts, adversarial attacks, real world data is few-shot between all possible scene scenarios, test-time errors have larger safety implications).
weight: 4
axis: Implicit Criteria

criterion: The responses discusses how neural networks infer depth from learned patterns on visual context, and not direct measurement as LiDAR uses thus affecting down-stream generalizability.
weight: 4
axis: Synthesis of Information

criterion: The response discusses the regulatory and safety implications of choosing one mode over the other.
weight: 3
axis: Implicit Criteria

criterion: Considers specific failure modes for each system (e.g. LIDAR: adverse weather, solid-state faults, low-reflectivity targets, cameras/NNs: domain shifts, adversarial perturbations). 
weight: 5
axis: Implicit Criteria

criterion: The response highlights recent or emerging technologies (e.g., solid-state LiDAR, vision transformers, NeRFs, Gaussian splatting)
weight: 3
axis: Implicit Criteria

criterion: The response includes at least one informative diagram contrasting how images are processed and used for downstream tasks between LiDAR and neural networks with cameras.
weight: 3
axis: Communication Quality

criterion: The response discusses trade-offs in energy consumption between LiDAR and neural networks with cameras (i.e., LiDAR draws more power at the sensor level sense it must generate its own light-source while camera + neural networks generally use ambient lighting, but at the computational level, fusion architectures between cameras and neural networks require significantly more floating point operations for forward passes as opposed to LiDAR-associated point cloud computations).
weight: 4
axis: Implicit Criteria

criterion: The response discusses key factors affecting compute requirements between LiDAR and cameras with neural networks (e.g., cameras + NNs requires more modules for low-light conditions, LiDAR compute scales with point cloud density versus image resolution, computes scales linearly with the number of cameras, compute scales linearly with number of sweeps for LiDAR). 
weight: 4
axis: Implicit Criteria

criterion: The response clearly explains and contrasts the rationale behind prioritizing LiDAR versus camera-based neural-network systems or vice versa for autonomous vehicles.
weight: 5
axis: Explicit Criteria

criterion: The response includes empirical data comparing LiDAR vs. cameras and neural networks over varying metrics (e.g., prediction accuracy, latency, FLOPs, velocity).
weight: 3
axis: Implicit Criteria

criterion: The response describes where and how perception modules (LiDAR or NN-based vision) sit within the full AV stack (i.e., perception sits between sensor preprocessing and prediction/planning, ingesting sensor data and labeling relevant views (lanes, traffic signals, other cars, hazardous objects, etc.) which are then used by downstream modules).
weight: 4
axis: Implicit Criteria

criterion: Proposes or references a benchmark protocol or experiment that compares LiDAR and camera-only systems on the same tasks.
weight: 5
axis: Explicit Criteria

criterion: The response considers regional or legal factors which could influence sensor choice (e.g., energy regulations on compute, safety regulations on predictive models, auditing issues due to challenging explainability of neural networks).
weight: 3
axis: Implicit Criteria

criterion: The response mentions software stacks and toolkits used to process or simulate sensor data (e.g., ROS, Apollo, Autoware, NVIDIA DRIVEworks).
weight: 2
axis: Implicit Criteria

criterion: The response does not clearly define units or context when using common descriptors to discuss these technical systems (e.g., "expensive," "efficiency," "computationally heavy," "latency"). 
weight: -3
axis: Implicit Criteria

criterion: The response mentions neural networks without describing the particular architecture (e.g., transformer, recurrent neural network, convolutional networks, state space models). 
weight: -2
axis: Implicit Criteria

criterion: The response correctly indicates that LiDAR and cameras + neural networks are not strictly opposing strategies and that AVs equipped with LiDAR frequently also come equipped with cameras and some degree of neural networks.
weight: 5
axis: Synthesis of Information

criterion: The response indicates which AV manufacturers use LiDAR and cameras with neural networks (LiDAR-equipped AVs: GM Cruise, Waymo, Aurora, non-LiDAR: Tesla). 
weight: 5
axis: Explicit Criteria

criterion: The response mentions the price-point of LiDAR is a key limiting factor for Tesla. 
weight: 5
axis: Implicit Criteria