斯坦福大学Junior自动驾驶车辆：城市挑战赛亚军

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"Junior 08自动驾驶 - 斯坦福大学在DARPA城市挑战中的参赛车辆" 本文介绍了Junior，一辆能够自主导航城市环境的机器人车辆。Junior能够在城市环境中选择自己的路线，感知并与其他交通参与者互动，执行包括变道、掉头、停车和并入移动车流在内的各种城市驾驶技能。这辆车辆在由美国政府组织的DARPA（国防高级研究计划局）城市挑战赛中成功完成比赛，并获得了第二名。 Junior的开发团队由斯坦福人工智能实验室、大众美国电子研究实验室、英特尔研究以及罗伯特·博世公司等多个机构的成员组成。团队成员在自动驾驶技术方面具有深厚的背景和专业知识。文章的摘要部分主要涉及以下几个关键知识点： 1. **自动驾驶系统架构**：Junior的系统设计包含了路径规划、感知与交互、驾驶技能执行等核心模块。这些模块共同构成了一个复杂的自动驾驶系统，使车辆能够在复杂的城市环境中安全行驶。 2. **路径规划**：Junior能够自主选择行驶路线，这涉及到高精度的地图数据、实时的定位技术和智能的路径决策算法。这些算法需要考虑交通规则、道路条件和实时交通信息。 3. **环境感知**：通过搭载的传感器（如激光雷达、摄像头、雷达等），Junior能够识别周围的车辆、行人和其他障碍物。这种感知能力对于安全驾驶至关重要，它需要处理大量的传感器数据并进行实时分析。 4. **交互与决策**：Junior不仅需要识别环境，还需要理解其他交通参与者的意图，并据此做出相应的驾驶决策。这涉及到复杂的机器学习算法和行为预测模型。 5. **驾驶技能执行**：Junior能够执行多种城市驾驶技能，如变道、U型转弯、停车和并入移动车流。这些技能的实现需要精确的车辆控制和动态规划。 6. **DARPA城市挑战赛**：这是一个推动自动驾驶技术发展的重大赛事，Junior在此比赛中取得的成果展示了其自动驾驶系统的成熟度和技术实力。 7. **合作研发**：Junior的研发涉及多个顶尖研究机构，这表明自动驾驶技术的发展是跨学科、多领域协作的结果，也反映了产业界对这一领域的重视。 Junior的自动驾驶系统代表了当时最先进的技术，其设计和实施涵盖了自动驾驶的多个关键领域，为后续的自动驾驶研究和应用奠定了基础。

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Figure 3: Flow diagram of the Junior Software.

An alternative to comparing vertical displacements is to compare the range returned by two adja-

cent beams, where “adjacency” is measured in terms of the pointing angle of the beams. Each of

the 64 lasers has a ﬁ xed pitch angle relative to the vehicle frame, and thus would sweep out a circle

of a ﬁxed radius on a ﬂat ground plane as the sensor rotates. Sloped terrain locally compresses

these rings, causing the distance between adjacent rings to be smaller than the inter-ring distance

on ﬂat terrain. In the extreme case, a vertical obstacle causes adjacent beams to return nearly equal

ranges. Because the individual beams strike the ground at suchshallowangles,thedistancebe-

tween rings is a much more sensitive measurement of terrain slope than vertical displacement. By

ﬁnding points that generate inter-ring distances that differ from the expected distance by more than

a given threshold, even obstacles that are not apparent to theverticalthresholdingalgorithmcan

be reliably detected.

In addition to terrain s lope, rolling and pitching of the vehicle will cause the rings traced out by the

individual lasers to compress and expand. If this is not takenintoaccount,rollingtotheleftcan

cause otherwise ﬂat terrain to the left of the vehicle to be detected incorrectly as an obstacle. This

problem can be remedied by making the expected distance to thenextringafunctionofrange,

rather than the index of the particular laser. Thus as the vehicle rolls to the left, the expected range

difference for a speciﬁc beam decreases as the ring moves closer to the vehicle. Implemented in

this way, small obstacles can be reliably detected even as thesensorrollsandpitches.

(a) (b)

Figure 4: (a) The Velodyne contains 64 laser sensors and rotates at 10 Hz. It is able to see objects

and terrain out to 60 meters in every direction. (b) The IBEO sensor possesses four scan lines which

are primarily parallel to the ground. The IBEO is capable of detecting large vertical obstacles, such

as cars and signposts.

(a) (b)

Figure 5: Obstacles detected by the vehicle are overlayed over aerial imagery (left) and Velodyne

data (right). In the example on the right, the curbs along bothsidesoftheroadaredetected.

Two more issues must be addressed when performing obstacle detection in urban terrain. First,

trees and other objects frequently overhang safe driving surfaces and should not be detected as

obstacles. Overhanging objects are ﬁltered out by comparingtheirheightwithasimpleground

model. Points tha t fall in a particular x-y grid cell that exceed the height of the lowest detected

point in the same cell by more than a given threshold (the height of the vehicle plus a safety buffer),

are ignored as overhanging obstacles.

Second, the Velodyne sensor possesses a “blind spot” behind the vehicle. This is the result of

the sensor’s geometry and mounting location. Further, it also cannot detect small obstacles such

as curbs in the immediate vicinity of the robot due to self-occlusion. Here the IBEO and SICK

LDLRS sensors are used to supplement the Velodyne data. Becausebothofthesesensorsarees-

sentially 2-D, ground readings cannot be distinguished fromverticalobstacles,andhenceobstacles

can only be found at very short range (where ground measurements are unlikely). Whenever either

of these sensors detects an object within a close range (15 meters for the LDLRS and 5 meters

for the IBEO), the measurement is ﬂagged as an obstacle. This combination between short-range

sensing in 2-D and longer range sensing using the 3-D sensor provides high reliability. We note

that a 5 meter cut-off for the IBEO sensor may seem overly pessimistic, a s this laser is de s igned

for long range detection (100 meters and more). However, the sensor presents a large number of

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首先写作一篇自我介绍的英文文章，然后编写程序，先追加一行姓名、学号到原有文件，然后读取文档中的全部内容，并打印输出。效果如图所示。注意：文件打开模式的选择，文件及时关闭，可以按行读取文件内容。