Learning a human-like driving policy from large-scale driving demonstrations
is promising, but the uncertainty and non-deterministic nature of planning make
it challenging. In this work, to cope with the uncertainty problem, we propose
VADv2, an end-to-end driving model based on probabilistic planning. VADv2 takes
multi-view image sequences as input in a streaming manner, transforms sensor
data into environmental token embeddings, outputs the probabilistic
distribution of action, and samples one action to control the vehicle. Only
with camera sensors, VADv2 achieves state-of-the-art closed-loop performance on
the CARLA Town05 benchmark, significantly outperforming all existing methods.
It runs stably in a fully end-to-end manner, even without the rule-based
wrapper. Closed-loop demos are presented at this https URL

通过大规模驾驶示范学习人类驾驶行为模型，提出基于概率规划的 VADv2 端到端驾驶模型，解决不确定性问题，在 CARLA Town05 基准测试中表现优异，在没有基于规则的封装的情况下稳定运行。

VADv2: 基于概率规划的端到端矢量化自动驾驶

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic  Planning

Without manually annotated identities, unsupervised multi-object trackers are
inferior to learning reliable feature embeddings. It causes the
similarity-based inter-frame association stage also be error-prone, where an
uncertainty problem arises. The frame-by-frame accumulated uncertainty prevents
trackers from learning the consistent feature embedding against time variation.
To avoid this uncertainty problem, recent self-supervised techniques are
adopted, whereas they failed to capture temporal relations. The interframe
uncertainty still exists. In fact, this paper argues that though the
uncertainty problem is inevitable, it is possible to leverage the uncertainty
itself to improve the learned consistency in turn. Specifically, an
uncertainty-based metric is developed to verify and rectify the risky
associations. The resulting accurate pseudo-tracklets boost learning the
feature consistency. And accurate tracklets can incorporate temporal
information into spatial transformation. This paper proposes a tracklet-guided
augmentation strategy to simulate tracklets' motion, which adopts a
hierarchical uncertainty-based sampling mechanism for hard sample mining. The
ultimate unsupervised MOT framework, namely U2MOT, is proven effective on
MOT-Challenges and VisDrone-MOT benchmark. U2MOT achieves a SOTA performance
among the published supervised and unsupervised trackers.

通过开发一个基于不确定性的度量标准，以验证和矫正风险的关联，本论文提出了一种通过模拟 tracklets 运动来增强学习特征一致性的 tracklet-guided 增强策略，同时采用分层不确定性采样机制进行难样本挖掘。最终的无监督 MOT 框架，即 U2MOT，在 MOT-Challenges 和 VisDrone-MOT 基准测试中被证明有效，取得了领先于其他已发表的监督和无监督跟踪器的性能。