In an era marked by the rapid scaling of foundation models, autonomous
driving technologies are approaching a transformative threshold where
end-to-end autonomous driving (E2E-AD) emerges due to its potential of scaling
up in the data-driven manner. However, existing E2E-AD methods are mostly
evaluated under the open-loop log-replay manner with L2 errors and collision
rate as metrics (e.g., in nuScenes), which could not fully reflect the driving
performance of algorithms as recently acknowledged in the community. For those
E2E-AD methods evaluated under the closed-loop protocol, they are tested in
fixed routes (e.g., Town05Long and Longest6 in CARLA) with the driving score as
metrics, which is known for high variance due to the unsmoothed metric function
and large randomness in the long route. Besides, these methods usually collect
their own data for training, which makes algorithm-level fair comparison
infeasible.
To fulfill the paramount need of comprehensive, realistic, and fair testing
environments for Full Self-Driving (FSD), we present Bench2Drive, the first
benchmark for evaluating E2E-AD systems' multiple abilities in a closed-loop
manner. Bench2Drive's official training data consists of 2 million fully
annotated frames, collected from 10000 short clips uniformly distributed under
44 interactive scenarios (cut-in, overtaking, detour, etc), 23 weathers (sunny,
foggy, rainy, etc), and 12 towns (urban, village, university, etc) in CARLA v2.
Its evaluation protocol requires E2E-AD models to pass 44 interactive scenarios
under different locations and weathers which sums up to 220 routes and thus
provides a comprehensive and disentangled assessment about their driving
capability under different situations. We implement state-of-the-art E2E-AD
models and evaluate them in Bench2Drive, providing insights regarding current
status and future directions.

自动驾驶技术正逐渐进入一个转折点，在数据驱动的方式下，通过端到端自动驾驶 (E2E-AD) 可扩展性，已经出现了一个具备变革性潜力的时代。然而，现有的 E2E-AD 方法主要是通过开环日志重放方式进行评估，使用 L2 误差和碰撞率作为度量标准，但这无法充分反映算法的驾驶表现。为了满足全自动驾驶 (FSD) 的全面、真实和公平的测试环境的首要需求，我们提出了 Bench2Drive，这是第一个在闭环方式下评估 E2E-AD 系统多项能力的基准测试。

Bench2Drive：闭环端到端自主驾驶的多能力基准测试

Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop  End-To-End Autonomous Driving

Current autonomous driving technologies are being rolled out in geo-fenced
areas with well-defined operation conditions such as time of operation, area,
weather conditions and road conditions. In this way, challenging conditions as
adverse weather, slippery road or densely-populated city centers can be
excluded. In order to lift the geo-fenced restriction and allow a more dynamic
availability of autonomous driving functions, it is necessary for the vehicle
to autonomously perform an environment condition assessment in real time to
identify when the system cannot operate safely and either stop operation or
require the resting passenger to take control. In particular, adverse-weather
challenges are a fundamental limitation as sensor performance degenerates
quickly, prohibiting the use of sensors such as cameras to locate and monitor
road signs, pedestrians or other vehicles. To address this issue, we train a
deep learning model to identify outdoor weather and dangerous road conditions,
enabling a quick reaction to new situations and environments. We achieve this
by introducing an improved taxonomy and label hierarchy for a state-of-the-art
adverse-weather dataset, relabelling it with a novel semi-automated labeling
pipeline. Using the novel proposed dataset and hierarchy, we train RECNet, a
deep learning model for the classification of environment conditions from a
single RGB frame. We outperform baseline models by relative 16% in F1- Score,
while maintaining a real-time capable performance of 20 Hz.

通过改进标注流程和提出的分类体系，我们训练了一个深度学习模型来识别室外天气和危险的路况，从而实现对新情景和环境的快速反应。我们在 F1 得分上相对于基线模型提高了 16%，同时保持了 20 Hz 的实时性能。